def generate_file_name(args, subject_number, which_layer, glove=False, word2vec=False, baseline=False):
direction, validate, rlabel, elabel, glabel, w2vlabel, bertlabel, plabel, prlabel = helper.generate_labels(args)
if args.bert or args.word2vec or args.glove:
specific_file = str(plabel) + str(prlabel) + str(rlabel) + str(elabel) + str(glabel) + str(w2vlabel) + str(
bertlabel) + str(direction) + str(validate) + "-subj{}-{}_layer{}"
file_name = specific_file.format(
subject_number,
args.agg_type,
which_layer
)
elif baseline:
if glove:
file_name = "glove" + str(direction) + str(validate) + "-subj{}-avg_layer1".format(
subject_number
)
elif word2vec:
file_name = "word2vec" + str(direction) + str(validate) + "-subj{}-avg_layer1".format(
subject_number
)
else:
specific_file = str(direction) + str(validate) + "-subj{}-parallel-english-to-{}-model-{}layer-{}-pred-layer{}-{}"
file_name = specific_file.format(
subject_number,
args.language,
4,
"brnn",
which_layer,
args.agg_type
)
else:
return "ERROR"
return file_name
def main():
global temp_file_name
argparser = argparse.ArgumentParser(
description="Decoding (linear reg). step for correlating NN and brain")
argparser.add_argument('--embedding_layer',
type=str,
help="Location of NN embedding (for a layer)",
required=True)
argparser.add_argument(
"--rsa",
action='store_true',
default=False,
help="True if RSA is used to generate residual values")
argparser.add_argument(
"--brain_to_model",
action='store_true',
default=False,
help="True if regressing brain to model, False if not")
argparser.add_argument(
"--model_to_brain",
action='store_true',
default=False,
help="True if regressing model to brain, False if not")
argparser.add_argument(
"--which_layer",
help="Layer of interest in [1: total number of layers]",
type=int,
default=1)
argparser.add_argument("--cross_validation",
action='store_true',
default=True,
help="True if add cross validation, False if not")
argparser.add_argument("--subject_number",
type=int,
default=1,
help="subject number (fMRI data) for decoding")
argparser.add_argument(
"--random",
action='store_true',
default=False,
help="True if initialize random brain activations, False if not")
argparser.add_argument(
"--rand_embed",
action='store_true',
default=False,
help="True if initialize random embeddings, False if not")
argparser.add_argument(
"--glove",
action='store_true',
default=False,
help="True if initialize glove embeddings, False if not")
argparser.add_argument(
"--word2vec",
action='store_true',
default=False,
help="True if initialize word2vec embeddings, False if not")
argparser.add_argument(
"--bert",
action='store_true',
default=False,
help="True if initialize bert embeddings, False if not")
argparser.add_argument(
"--normalize",
action='store_true',
default=False,
help="True if add normalization across voxels, False if not")
argparser.add_argument("--permutation",
action='store_true',
default=False,
help="True if permutation, False if not")
argparser.add_argument(
"--permutation_region",
action='store_true',
default=False,
help="True if permutation by brain region, False if not")
argparser.add_argument("--add_bias",
action='store_true',
default=True,
help="True if add bias, False if not")
argparser.add_argument("--llh",
action='store_true',
default=True,
help="True if calculate likelihood, False if not")
argparser.add_argument("--ranking",
action='store_true',
default=False,
help="True if calculate ranking, False if not")
argparser.add_argument(
"--mixed_effects",
action='store_true',
default=False,
help="True if calculate mixed effects, False if not")
### UPDATE FILE PATHS HERE ###
argparser.add_argument(
"--fmri_path",
default="/n/shieber_lab/Lab/users/cjou/fmri/",
type=str,
help="file path to fMRI data on the Odyssey cluster")
argparser.add_argument(
"--to_save_path",
default="/n/shieber_lab/Lab/users/cjou/",
type=str,
help="file path to and create rmse/ranking/llh on the Odyssey cluster")
### UPDATE FILE PATHS HERE ###
args = argparser.parse_args()
if not args.glove and not args.word2vec and not args.bert and not args.rand_embed:
embed_loc = args.embedding_layer
file_name = embed_loc.split("/")[-1].split(".")[0]
embedding = scipy.io.loadmat(embed_loc)
embed_matrix = helper.get_embed_matrix(embedding)
else:
embed_loc = args.embedding_layer
file_name = embed_loc.split(
"/")[-1].split(".")[0].split("-")[-1] + "_layer" + str(
args.which_layer) # aggregation type + which layer
embed_matrix = np.array(pickle.load(open(embed_loc, "rb")))
direction, validate, rlabel, elabel, glabel, w2vlabel, bertlabel, plabel, prlabel = helper.generate_labels(
args)
# get modified activations
activations = pickle.load(
open(
"{}subj{}/activations.p".format(args.fmri_path,
args.subject_number), "rb"))
volmask = pickle.load(
open("{}subj{}/volmask.p".format(args.fmri_path, args.subject_number),
"rb"))
modified_activations = pickle.load(
open(
"{}subj{}/modified_activations.p".format(args.fmri_path,
args.subject_number),
"rb"))
print("PERMUTATION: " + str(args.permutation))
print("PERMUTATION REGION: " + str(args.permutation_region))
print("PLABEL: " + str(plabel))
print("PRLABEL: " + str(prlabel))
if args.normalize:
modified_activations = helper.z_score(modified_activations)
embed_matrix = helper.z_score(embed_matrix)
if args.random:
print("RANDOM ACTIVATIONS")
modified_activations = np.random.randint(-20,
high=20,
size=(240, 79, 95, 68))
# make file path
if not os.path.exists('{}residuals_od32/'.format(args.to_save_path)):
os.makedirs('{}residuals_od32/'.format(args.to_save_path))
if not os.path.exists('{}final_rankings/'.format(args.to_save_path)):
os.makedirs('{}final_rankings/'.format(args.to_save_path))
if not os.path.exists('{}rsa/'.format(args.to_save_path)):
os.makedirs('{}rsa/'.format(args.to_save_path))
if not os.path.exists('{}nested_llh/'.format(args.to_save_path)):
os.makedirs('{}nested_llh/'.format(args.to_save_path))
temp_file_name = str(plabel) + str(prlabel) + str(rlabel) + str(
elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(
direction) + str(validate) + "-subj" + str(
args.subject_number) + "-" + str(file_name) + "_no_spotlight"
if args.mixed_effects:
val = mixed_effects_analysis(args, embed_matrix)
else:
all_residuals, llhs, rankings = all_activations_for_all_sentences(
modified_activations, volmask, embed_matrix, args)
# dump
if args.rsa:
file_name = '{}rsa/'.format(
args.to_save_path) + str(temp_file_name) + ".p"
pickle.dump(all_residuals, open(file_name, "wb"))
else:
if args.llh:
llh_file_name = '{}nested_llh/'.format(
args.to_save_path) + temp_file_name
print("LLH SPOTLIGHTS FILE: " + str(llh_file_name))
pickle.dump(llhs,
open(llh_file_name + "-llh.p", "wb"),
protocol=-1)
altered_file_name = '{}residuals_od32/'.format(
args.to_save_path) + temp_file_name
print("RESIDUALS FILE: " + str(altered_file_name))
pickle.dump(all_residuals,
open(altered_file_name + ".p", "wb"),
protocol=-1)
if args.model_to_brain and args.ranking:
ranking_file_name = '{}final_rankings/'.format(
args.to_save_path) + temp_file_name
print("RANKING FILE: " + str(ranking_file_name))
pickle.dump(rankings,
open(ranking_file_name + ".p", "wb"),
protocol=-1)
print("done.")
return
def main():
global temp_file_name
argparser = argparse.ArgumentParser(
description="Decoding (linear reg). step for correlating NN and brain")
argparser.add_argument('--embedding_layer',
type=str,
help="Location of NN embedding (for a layer)",
required=True)
argparser.add_argument(
"--rsa",
action='store_true',
default=False,
help="True if RSA is used to generate residual values")
argparser.add_argument("--subject_mat_file", type=str, help=".mat file ")
argparser.add_argument(
"--brain_to_model",
action='store_true',
default=False,
help="True if regressing brain to model, False if not")
argparser.add_argument(
"--model_to_brain",
action='store_true',
default=True,
help="True if regressing model to brain, False if not")
argparser.add_argument(
"--which_layer",
help="Layer of interest in [1: total number of layers]",
type=int,
default=1)
argparser.add_argument("--cross_validation",
action='store_true',
default=True,
help="True if add cross validation, False if not")
argparser.add_argument(
"--random",
action='store_true',
default=False,
help="True if initialize random brain activations, False if not")
argparser.add_argument(
"--rand_embed",
action='store_true',
default=False,
help="True if initialize random embeddings, False if not")
argparser.add_argument(
"--glove",
action='store_true',
default=False,
help="True if initialize glove embeddings, False if not")
argparser.add_argument(
"--word2vec",
action='store_true',
default=False,
help="True if initialize word2vec embeddings, False if not")
argparser.add_argument(
"--bert",
action='store_true',
default=False,
help="True if initialize bert embeddings, False if not")
argparser.add_argument(
"--normalize",
action='store_true',
default=True,
help="True if add normalization across voxels, False if not")
argparser.add_argument("--permutation",
action='store_true',
default=False,
help="True if permutation, False if not")
argparser.add_argument(
"--permutation_region",
action='store_true',
default=False,
help="True if permutation by brain region, False if not")
argparser.add_argument("--add_bias",
action='store_true',
default=True,
help="True if add bias, False if not")
argparser.add_argument("--llh",
action='store_true',
default=True,
help="True if calculate likelihood, False if not")
argparser.add_argument("--ranking",
action='store_true',
default=True,
help="True if calculate ranking, False if not")
argparser.add_argument(
"--mixed_effects",
action='store_true',
default=True,
help="True if calculate mixed effects, False if not")
argparser.add_argument("--local",
action='store_true',
default=False,
help="True if local, False if not")
argparser.add_argument(
"--batch_num",
type=int,
help="batch number of total (for scripting) (out of --total_batches)",
required=True)
argparser.add_argument("--total_batches",
type=int,
help="total number of batches",
default=100)
args = argparser.parse_args()
if not args.glove and not args.word2vec and not args.bert and not args.rand_embed:
embed_loc = args.embedding_layer
file_name = embed_loc.split("/")[-1].split(".")[0]
embedding = scipy.io.loadmat(embed_loc)
embed_matrix = helper.get_embed_matrix(embedding)
else:
embed_loc = args.embedding_layer
file_name = embed_loc.split(
"/")[-1].split(".")[0].split("-")[-1] + "_layer" + str(
args.which_layer) # aggregation type + which layer
embed_matrix = np.array(pickle.load(open(embed_loc, "rb")))
direction, validate, rlabel, elabel, glabel, w2vlabel, bertlabel, plabel, prlabel = helper.generate_labels(
args)
print("PERMUTATION: " + str(args.permutation))
print("PERMUTATION REGION: " + str(args.permutation_region))
print("PLABEL: " + str(plabel))
print("PRLABEL: " + str(prlabel))
# normalize
embed_matrix = helper.z_score(embed_matrix)
# PCA
pca = PCA(0.75)
embed_matrix = pca.fit_transform(embed_matrix)
print("PCA SHAPE: " + str(embed_matrix.shape))
# make file path
if args.local:
if not os.path.exists('../mixed_effects/'):
os.makedirs('../mixed_effects/')
file_path = '../mixed_effects/'
else:
if not os.path.exists('/n/shieber_lab/Lab/users/cjou/mixed_effects/'):
os.makedirs('/n/shieber_lab/Lab/users/cjou/mixed_effects/')
file_path = '/n/shieber_lab/Lab/users/cjou/mixed_effects/'
temp_file_name = str(plabel) + str(prlabel) + str(rlabel) + str(
elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(
direction) + str(validate) + "-" + str(
file_name) + "_mixed_effects_batch" + str(
args.batch_num) + "of" + str(args.total_batches)
# get residuals and predictions
# all_residuals, predictions, true_spotlights, llhs = all_activations_for_all_sentences(modified_activations, volmask, embed_matrix, args)
rmses = mixed_effects_analysis(args, embed_matrix)
# dump
# if args.llh:
# llh_file_name = "/n/shieber_lab/Lab/users/cjou/llh/" + temp_file_name
# print("LLH SPOTLIGHTS FILE: " + str(llh_file_name))
# pickle.dump( llhs, open(llh_file_name+"-llh.p", "wb" ), protocol=-1 )
altered_file_name = file_path + temp_file_name
print("RESIDUALS FILE: " + str(altered_file_name))
pickle.dump(rmses, open(altered_file_name + ".p", "wb"), protocol=-1)
# if args.model_to_brain and args.ranking:
# ranking_file_name = "/n/shieber_lab/Lab/users/cjou/final_rankings/" + temp_file_name
# print("RANKING FILE: " + str(ranking_file_name))
# pickle.dump( rankings, open(ranking_file_name + ".p", "wb" ), protocol=-1 )
print("done.")
return
def main():
global temp_file_name
argparser = argparse.ArgumentParser(description="Decoding (linear reg). step for correlating NN and brain")
argparser.add_argument('--embedding_layer', type=str, help="Location of NN embedding (for a layer)", required=True)
argparser.add_argument("--rsa", action='store_true', default=False, help="True if RSA is used to generate residual values")
argparser.add_argument("--subject_mat_file", type=str, help=".mat file ")
argparser.add_argument("--brain_to_model", action='store_true', default=False, help="True if regressing brain to model, False if not")
argparser.add_argument("--model_to_brain", action='store_true', default=False, help="True if regressing model to brain, False if not")
argparser.add_argument("--which_layer", help="Layer of interest in [1: total number of layers]", type=int, default=1)
argparser.add_argument("--cross_validation", action='store_true', default=True, help="True if add cross validation, False if not")
argparser.add_argument("--subject_number", type=int, default=1, help="subject number (fMRI data) for decoding")
argparser.add_argument("--batch_num", type=int, help="batch number of total (for scripting) (out of --total_batches)", required=True)
argparser.add_argument("--total_batches", type=int, help="total number of batches", required=True)
argparser.add_argument("--random", action='store_true', default=False, help="True if initialize random brain activations, False if not")
argparser.add_argument("--rand_embed", action='store_true', default=False, help="True if initialize random embeddings, False if not")
argparser.add_argument("--glove", action='store_true', default=False, help="True if initialize glove embeddings, False if not")
argparser.add_argument("--word2vec", action='store_true', default=False, help="True if initialize word2vec embeddings, False if not")
argparser.add_argument("--bert", action='store_true', default=False, help="True if initialize bert embeddings, False if not")
argparser.add_argument("--normalize", action='store_true', default=False, help="True if add normalization across voxels, False if not")
argparser.add_argument("--permutation", action='store_true', default=False, help="True if permutation, False if not")
argparser.add_argument("--permutation_region", action='store_true', default=False, help="True if permutation by brain region, False if not")
argparser.add_argument("--add_bias", action='store_true', default=True, help="True if add bias, False if not")
argparser.add_argument("--llh", action='store_true', default=True, help="True if calculate likelihood, False if not")
argparser.add_argument("--ranking", action='store_true', default=True, help="True if calculate ranking, False if not")
argparser.add_argument("--mixed_effects", action='store_true', default=False, help="True if calculate mixed effects, False if not")
argparser.add_argument("--significance", action='store_true', default=False, help="True if calculate significance, False if not")
args = argparser.parse_args()
if not args.glove and not args.word2vec and not args.bert and not args.rand_embed:
embed_loc = args.embedding_layer
file_name = embed_loc.split("/")[-1].split(".")[0]
embedding = scipy.io.loadmat(embed_loc)
embed_matrix = helper.get_embed_matrix(embedding)
else:
embed_loc = args.embedding_layer
file_name = embed_loc.split("/")[-1].split(".")[0].split("-")[-1] + "_layer" + str(args.which_layer) # aggregation type + which layer
embed_matrix = np.array(pickle.load( open( embed_loc , "rb" ) ))
# if args.word2vec:
# embed_matrix = pickle.load( open( "/n/shieber_lab/Lab/users/cjou/embeddings/word2vec/" + str(file_name) + ".p", "rb" ) )
# elif args.glove:
# embed_matrix = pickle.load( open( "/n/shieber_lab/Lab/users/cjou/embeddings/glove/" + str(file_name) + ".p", "rb" ) )
# elif args.bert:
# embed_matrix = pickle.load( open( "/n/shieber_lab/Lab/users/cjou/embeddings/bert/" + str(file_name) + ".p", "rb" ) )
# else: # args.rand_embed
# embed_matrix = pickle.load( open( "/n/shieber_lab/Lab/users/cjou/embeddings/rand_embed/rand_embed.p", "rb" ) )
subj_num = args.subject_number
num = args.batch_num
total_batches = args.total_batches
direction, validate, rlabel, elabel, glabel, w2vlabel, bertlabel, plabel, prlabel = helper.generate_labels(args)
# get modified activations
activations = pickle.load( open( f"/n/shieber_lab/Lab/users/cjou/fmri/subj{subj_num}/activations.p", "rb" ) )
volmask = pickle.load( open( f"/n/shieber_lab/Lab/users/cjou/fmri/subj{subj_num}/volmask.p", "rb" ) )
modified_activations = pickle.load( open( f"/n/shieber_lab/Lab/users/cjou/fmri/subj{subj_num}/modified_activations.p", "rb" ) )
print("PERMUTATION: " + str(args.permutation))
print("PERMUTATION REGION: " + str(args.permutation_region))
print("PLABEL: " + str(plabel))
print("PRLABEL: " + str(prlabel))
if args.normalize:
modified_activations = helper.z_score(modified_activations)
# embed_matrix = helper.z_score(embed_matrix)
if args.random:
print("RANDOM ACTIVATIONS")
modified_activations = np.random.randint(-20, high=20, size=(240, 79, 95, 68))
# make file path
if not os.path.exists('/n/shieber_lab/Lab/users/cjou/residuals_od32/'):
os.makedirs('/n/shieber_lab/Lab/users/cjou/residuals_od32/')
if not os.path.exists('/n/shieber_lab/Lab/users/cjou/predictions_od32/'):
os.makedirs('/n/shieber_lab/Lab/users/cjou/predictions_od32/')
if not os.path.exists('/n/shieber_lab/Lab/users/cjou/true_spotlights_od32/'):
os.makedirs('/n/shieber_lab/Lab/users/cjou/true_spotlights_od32/')
if not os.path.exists('/n/shieber_lab/Lab/users/cjou/rsa/'):
os.makedirs('/n/shieber_lab/Lab/users/cjou/rsa/')
if not os.path.exists('/n/shieber_lab/Lab/users/cjou/llh/'):
os.makedirs('/n/shieber_lab/Lab/users/cjou/llh/')
if not os.path.exists('/n/shieber_lab/Lab/users/cjou/alphas/'):
os.makedirs('/n/shieber_lab/Lab/users/cjou/alphas/')
temp_file_name = str(plabel) + str(prlabel) + str(rlabel) + str(elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(direction) + str(validate) + "-subj" + str(args.subject_number) + "-" + str(file_name) + "_residuals_part" + str(args.batch_num) + "of" + str(args.total_batches)
# get residuals and predictions
# all_residuals, predictions, true_spotlights, llhs = all_activations_for_all_sentences(modified_activations, volmask, embed_matrix, args)
all_residuals, llhs, rankings, alphas = all_activations_for_all_sentences(modified_activations, volmask, embed_matrix, args)
# dump
if args.rsa:
file_name = "/n/shieber_lab/Lab/users/cjou/rsa/" + str(temp_file_name) + ".p"
pickle.dump( all_residuals, open(file_name, "wb" ) )
else:
if args.llh:
llh_file_name = "/n/shieber_lab/Lab/users/cjou/llh/" + temp_file_name
print("LLH SPOTLIGHTS FILE: " + str(llh_file_name))
pickle.dump( llhs, open(llh_file_name+"-llh.p", "wb" ), protocol=-1 )
altered_file_name = "/n/shieber_lab/Lab/users/cjou/residuals_od32/" + temp_file_name
print("RESIDUALS FILE: " + str(altered_file_name))
pickle.dump( all_residuals, open(altered_file_name + ".p", "wb" ), protocol=-1 )
altered_file_name = "/n/shieber_lab/Lab/users/cjou/alphas/" + temp_file_name
print("ALPHAS FILE: " + str(altered_file_name))
pickle.dump( alphas, open(altered_file_name + ".p", "wb" ), protocol=-1 )
# alphas_file_name = "/n/shieber_lab/Lab/users/cjou/alphas/" + temp_file_name
# print("ALPHAS FILE: " + str(alphas_file_name))
# pickle.dump( alphas, open(alphas_file_name + ".p", "wb" ), protocol=-1 )
if args.model_to_brain and args.ranking:
ranking_file_name = "/n/shieber_lab/Lab/users/cjou/final_rankings/" + temp_file_name
print("RANKING FILE: " + str(ranking_file_name))
pickle.dump( rankings, open(ranking_file_name + ".p", "wb" ), protocol=-1 )
# pred_file_name = "/n/shieber_lab/Lab/users/cjou/predictions_od32/" + temp_file_name
# print("PREDICTIONS FILE: " + str(pred_file_name))
# pickle.dump( predictions, open(pred_file_name+"-decoding-predictions.p", "wb" ), protocol=-1 )
# spot_file_name = "/n/shieber_lab/Lab/users/cjou/true_spotlights_od32/" + temp_file_name
# print("TRUE SPOTLIGHTS FILE: " + str(spot_file_name))
# pickle.dump( true_spotlights, open(spot_file_name+"-true-spotlights.p", "wb" ), protocol=-1 )
print("done.")
return
def main():
argparser = argparse.ArgumentParser(description="plot RMSE by location")
argparser.add_argument(
"-language",
"--language",
help=
"Target language ('spanish', 'german', 'italian', 'french', 'swedish')",
type=str,
default='spanish')
argparser.add_argument("-num_layers",
"--num_layers",
help="Total number of layers ('2', '4')",
type=int,
default=2)
argparser.add_argument("-model_type",
"--model_type",
help="Type of model ('brnn', 'rnn')",
type=str,
default='brnn')
argparser.add_argument(
"-which_layer",
"--which_layer",
help="Layer of interest in [1: total number of layers]",
type=int,
default=1)
argparser.add_argument(
"-agg_type",
"--agg_type",
help="Aggregation type ('avg', 'max', 'min', 'last')",
type=str,
default='avg')
argparser.add_argument("-subject_number",
"--subject_number",
type=int,
default=1,
help="subject number (fMRI data) for decoding")
argparser.add_argument("-cross_validation",
"--cross_validation",
help="Add flag if add cross validation",
action='store_true',
default=False)
argparser.add_argument("-brain_to_model",
"--brain_to_model",
help="Add flag if regressing brain to model",
action='store_true',
default=False)
argparser.add_argument("-model_to_brain",
"--model_to_brain",
help="Add flag if regressing model to brain",
action='store_true',
default=False)
argparser.add_argument(
"-glove",
"--glove",
action='store_true',
default=False,
help="True if initialize glove embeddings, False if not")
argparser.add_argument(
"-word2vec",
"--word2vec",
action='store_true',
default=False,
help="True if initialize word2vec embeddings, False if not")
argparser.add_argument(
"-random",
"--random",
action='store_true',
default=False,
help="True if initialize random brain activations, False if not")
argparser.add_argument(
"-rand_embed",
"--rand_embed",
action='store_true',
default=False,
help="True if initialize random embeddings, False if not")
argparser.add_argument(
"-bert",
"--bert",
action='store_true',
default=False,
help="True if initialize bert embeddings, False if not")
argparser.add_argument("-permutation",
"--permutation",
action='store_true',
default=False,
help="True if permutation, False if not")
argparser.add_argument(
"-permutation_region",
"--permutation_region",
action='store_true',
default=False,
help="True if permutation by brain region, False if not")
argparser.add_argument("-local",
"--local",
action='store_true',
default=False,
help="True if running locally")
argparser.add_argument("-hard_drive",
"--hard_drive",
action='store_true',
default=False,
help="True if running from hard drive")
args = argparser.parse_args()
# get residuals
# check conditions // can remove when making pipeline
if args.brain_to_model and args.model_to_brain:
print("select only one flag for brain_to_model or model_to_brain")
exit()
if not args.brain_to_model and not args.model_to_brain:
print("select at least flag for brain_to_model or model_to_brain")
exit()
direction, validate, rlabel, elabel, glabel, w2vlabel, bertlabel, plabel, prlabel = helper.generate_labels(
args)
# residual_file = sys.argv[1]
file_loc = str(plabel) + str(prlabel) + str(rlabel) + str(elabel) + str(
glabel) + str(w2vlabel) + str(bertlabel) + str(direction) + str(
validate
) + "subj{}_parallel-english-to-{}-model-{}layer-{}-pred-layer{}-{}"
file_name = file_loc.format(args.subject_number, args.language,
args.num_layers, args.model_type,
args.which_layer, args.agg_type)
residual_file = "../rmses/concatenated-" + str(file_name) + ".p"
# file_name = residual_file.split("/")[-1].split(".")[0]
all_residuals = pickle.load(open(residual_file, "rb"))
# get atlas and roi
if not args.local:
atlas_vals = pickle.load(
open(
f"/n/shieber_lab/Lab/users/cjou/fmri/subj{args.subject_number}/atlas_vals.p",
"rb"))
atlas_labels = pickle.load(
open(
f"/n/shieber_lab/Lab/users/cjou/fmri/subj{args.subject_number}/atlas_labels.p",
"rb"))
roi_vals = pickle.load(
open(
f"/n/shieber_lab/Lab/users/cjou/fmri/subj{args.subject_number}/roi_vals.p",
"rb"))
roi_labels = pickle.load(
open(
f"/n/shieber_lab/Lab/users/cjou/fmri/subj{args.subject_number}/roi_labels.p",
"rb"))
elif args.hard_drive:
atlas_vals = pickle.load(
open(
f"/Volumes/passport/\!RESEARCH/examplesGLM/subj{args.subject_number}/atlas_vals.p",
"rb"))
atlas_labels = pickle.load(
open(
f"/Volumes/passport/\!RESEARCH/examplesGLM/subj{args.subject_number}/atlas_labels.p",
"rb"))
roi_vals = pickle.load(
open(
f"/Volumes/passport/\!RESEARCH/examplesGLM/subj{args.subject_number}/roi_vals.p",
"rb"))
roi_labels = pickle.load(
open(
f"/Volumes/passport/\!RESEARCH/examplesGLM/subj{args.subject_number}/roi_labels.p",
"rb"))
else:
atlas_vals = pickle.load(
open(f"../examplesGLM/subj{args.subject_number}/atlas_vals.p",
"rb"))
atlas_labels = pickle.load(
open(f"../examplesGLM/subj{args.subject_number}/atlas_labels.p",
"rb"))
roi_vals = pickle.load(
open(f"../examplesGLM/subj{args.subject_number}/roi_vals.p", "rb"))
roi_labels = pickle.load(
open(f"../examplesGLM/subj{args.subject_number}/roi_labels.p",
"rb"))
print("INITIAL:")
print(len(atlas_vals))
print(len(atlas_labels))
print(len(roi_vals))
print(len(roi_labels))
final_roi_labels = helper.clean_roi(roi_vals, roi_labels)
at_labels = helper.clean_atlas(atlas_vals, atlas_labels)
print("CLEANING")
print(len(final_roi_labels))
print(len(at_labels))
if not os.path.exists('../visualizations/'):
os.makedirs('../visualizations/')
# make dataframe
print(len(list(range(len(all_residuals)))))
print(len(all_residuals))
print(len(at_labels))
print(len(final_roi_labels))
df_dict = {
'voxel_index': list(range(len(all_residuals))),
'residuals': all_residuals,
'atlas_labels': at_labels,
'roi_labels': final_roi_labels
}
df = pd.DataFrame(df_dict)
# create plots
print("creating plots...")
# plot_roi(df, args, file_name + "-roi", zoom=False)
# plot_atlas(df, args, file_name + "-atlas", zoom=False)
# plot_roi(df, args, file_name + "-roi", zoom=True)
# plot_atlas(df, args, file_name + "-atlas", zoom=True)
plot_boxplot_for_roi(df, args, file_name + "-boxplot-roi")
# plot_boxplot_for_atlas(df, args, file_name + "-boxplot-atlas")
# plot_violinplot_for_roi(df, args, file_name + "-violinplot-roi")
# plot_violinplot_for_atlas(df, args, file_name + "-violinplot-atlas")
# plot_aggregations(df, args, file_name + "-agg")
print("done.")
return
def save_script(args):
if args.local:
if not os.path.exists('../decoding_scripts/'):
os.makedirs('../decoding_scripts/')
else:
if not os.path.exists('../../decoding_scripts/'):
os.makedirs('../../decoding_scripts/')
# file name assignments
direction, validate, rlabel, elabel, glabel, w2vlabel, bertlabel, plabel, prlabel = helper.generate_labels(
args)
if args.rsa:
rsa_label = "_rsa_"
direction = ""
validate = ""
else:
rsa_label = ""
# create subfolder
if not args.bert and not args.glove and not args.word2vec:
model_type = str(plabel) + str(prlabel) + str(rlabel) + str(
elabel
) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(
direction
) + str(validate) + str(
rsa_label
) + "subj{}_parallel-english-to-{}-model-{}layer-{}-pred-layer{}-{}"
folder_name = model_type.format(args.subject_number, args.language,
args.num_layers, args.model_type,
args.which_layer, args.agg_type)
print(folder_name)
master_script = "parallel-english-to-{}-model-{}layer-{}-pred-".format(
args.language, args.num_layers, args.model_type)
layer_script = "layer{}-".format(args.which_layer)
elif args.bert:
model_type = str(plabel) + str(prlabel) + str(rlabel) + str(
elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(
direction) + str(validate) + str(
rsa_label) + "subj{}_layer{}_{}"
folder_name = model_type.format(args.subject_number, args.which_layer,
args.agg_type)
print(folder_name)
master_script = ""
layer_script = "layer{}_".format(args.which_layer)
else:
model_type = str(plabel) + str(prlabel) + str(rlabel) + str(
elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(
direction) + str(validate) + str(rsa_label) + "subj{}_{}"
folder_name = model_type.format(args.subject_number, args.agg_type)
print(folder_name)
master_script = ""
layer_script = ""
if args.local:
if not os.path.exists('../decoding_scripts/' + str(folder_name) + '/'):
os.makedirs('../decoding_scripts/' + str(folder_name) + '/')
script_to_open = "../decoding_scripts/" + str(folder_name) + "/" + str(
folder_name) + ".sh"
else:
if not os.path.exists('../../decoding_scripts/' + str(folder_name) +
'/'):
os.makedirs('../../decoding_scripts/' + str(folder_name) + '/')
script_to_open = "../../decoding_scripts/" + str(
folder_name) + "/" + str(folder_name) + ".sh"
# make master script
with open(script_to_open, "w") as rsh:
rsh.write('''\
#!/bin/bash
for i in `seq 0 99`; do
sbatch "{}{}{}{}{}{}{}{}{}{}subj{}_decoding_""$i""_of_{}_{}{}{}.sh" -H
done
'''.format(plabel, prlabel, rlabel, elabel, glabel, w2vlabel, bertlabel,
rsa_label, direction, validate, args.subject_number,
args.total_batches, master_script, layer_script, args.agg_type))
# break into batches
for i in range(args.total_batches):
if not args.bert and not args.glove and not args.word2vec:
embedding_layer_location = "/n/shieber_lab/Lab/users/cjou/embeddings/parallel/{0}/{1}layer-{2}/{3}/parallel-english-to-{0}-model-{1}layer-{2}-pred-layer{4}-{3}.mat".format(
args.language, args.num_layers, args.model_type, args.agg_type,
args.which_layer)
file = str(plabel) + str(prlabel) + str(rlabel) + str(
elabel
) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(
direction
) + str(validate) + str(
rsa_label
) + "subj{}_decoding_{}_of_{}_parallel-english-to-{}-model-{}layer-{}-pred-layer{}-{}"
job_id = file.format(args.subject_number, i, args.total_batches,
args.language, args.num_layers,
args.model_type, args.which_layer,
args.agg_type)
elif args.bert:
embedding_layer_location = "/n/shieber_lab/Lab/users/cjou/embeddings/bert/layer{0}/{1}.p".format(
args.which_layer, args.agg_type)
file = str(plabel) + str(prlabel) + str(rlabel) + str(
elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(
direction) + str(validate) + str(
rsa_label) + "subj{}_decoding_{}_of_{}_layer{}_{}"
job_id = file.format(args.subject_number, i, args.total_batches,
args.which_layer, args.agg_type)
elif args.glove:
embedding_layer_location = "/n/shieber_lab/Lab/users/cjou/embeddings/glove/{0}.p".format(
args.agg_type)
file = str(plabel) + str(prlabel) + str(rlabel) + str(
elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(
direction) + str(validate) + str(
rsa_label) + "subj{}_decoding_{}_of_{}_{}"
job_id = file.format(args.subject_number, i, args.total_batches,
args.agg_type)
elif args.word2vec:
embedding_layer_location = "/n/shieber_lab/Lab/users/cjou/embeddings/word2vec/{0}.p".format(
args.agg_type)
file = str(plabel) + str(prlabel) + str(rlabel) + str(
elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(
direction) + str(validate) + str(
rsa_label) + "subj{}_decoding_{}_of_{}_{}"
job_id = file.format(args.subject_number, i, args.total_batches,
args.agg_type)
else:
print("error")
exit()
if args.local:
fname = '../decoding_scripts/' + str(folder_name) + '/' + str(
job_id) + '.sh'
else:
fname = '../../decoding_scripts/' + str(folder_name) + '/' + str(
job_id) + '.sh'
with open(fname, 'w') as rsh:
cvflag = "" if not args.cross_validation else " --cross_validation "
dflag = " --brain_to_model " if args.brain_to_model else " --model_to_brain "
pflag = "" if (plabel == "") else "--permutation"
prflag = "" if (prlabel == "") else "--permutation_region"
rflag = "" if (rlabel == "") else "--" + str(rlabel)
gflag = "" if (glabel == "") else "--" + str(glabel)
w2vflag = "" if (w2vlabel == "") else "--" + str(w2vlabel)
bertflag = "" if (bertlabel == "") else "--" + str(bertlabel)
eflag = "" if (elabel == "") else "--" + str(elabel)
memmap_flag = "" if not args.memmap else " --memmap"
rsaflag = "" if not args.rsa else " --rsa"
spotlight = "odyssey" if args.spotlight else "nested"
if args.rsa:
mem = "4500"
timelimit = "0-6:00"
elif args.llh:
mem = "9000"
timelimit = "0-24:00"
else:
mem = "7000"
timelimit = "0-7:00"
rsh.write('''\
#!/bin/bash
#SBATCH -J {0} # Job name
#SBATCH -p serial_requeue # partition (queue)
#SBATCH --mem {17} # memory pool for all cores
#SBATCH -t {18} # time (D-HH:MM)
#SBATCH --output=/n/home10/cjou/projects # file output location
#SBATCH -o ../../logs/outpt_{0}.txt # File that STDOUT writes to
#SBATCH -e ../../logs/err_{0}.txt # File that STDERR writes to
#SBATCH --mail-type=ALL
#SBATCH [email protected]
module load Anaconda3/5.0.1-fasrc02
source activate virtualenv
python ../../projects/opennmt-inspection/{19}_decoding.py \
--embedding_layer {1} \
--subject_mat_file /n/shieber_lab/Lab/users/cjou/fmri/subj{2}/examplesGLM.mat \
{3} {4} \
--subject_number {2} \
--batch_num {5} \
--total_batches {6} \
--which_layer {7} \
{8} {9} {10} {11} {12} {13} {14} {15} {16}
'''.format(job_id, embedding_layer_location, args.subject_number, dflag,
cvflag, i, args.total_batches, args.which_layer, rflag, eflag,
gflag, w2vflag, bertflag, pflag, prflag, memmap_flag, rsaflag, mem,
timelimit, spotlight))
def main():
argparser = argparse.ArgumentParser(
description="layer and subject group level comparison")
argparser.add_argument("-subject_number",
"--subject_number",
type=int,
default=1,
help="subject number (fMRI data) for decoding")
### SPECIFY MODEL PARAMETERS ###
argparser.add_argument("-cross_validation",
"--cross_validation",
help="Add flag if add cross validation",
action='store_true',
default=False)
argparser.add_argument("-brain_to_model",
"--brain_to_model",
help="Add flag if regressing brain to model",
action='store_true',
default=False)
argparser.add_argument("-model_to_brain",
"--model_to_brain",
help="Add flag if regressing model to brain",
action='store_true',
default=False)
argparser.add_argument(
"-agg_type",
"--agg_type",
help="Aggregation type ('avg', 'max', 'min', 'last')",
type=str,
default='avg')
argparser.add_argument(
"-language",
"--language",
help=
"Target language ('spanish', 'german', 'italian', 'french', 'swedish')",
type=str,
default='spanish')
argparser.add_argument("-num_layers",
"--num_layers",
help="Total number of layers ('2', '4')",
type=int,
required=True)
argparser.add_argument(
"-random",
"--random",
action='store_true',
default=False,
help="True if initialize random brain activations, False if not")
argparser.add_argument(
"-rand_embed",
"--rand_embed",
action='store_true',
default=False,
help="True if initialize random embeddings, False if not")
argparser.add_argument(
"-glove",
"--glove",
action='store_true',
default=False,
help="True if initialize glove embeddings, False if not")
argparser.add_argument(
"-word2vec",
"--word2vec",
action='store_true',
default=False,
help="True if initialize word2vec embeddings, False if not")
argparser.add_argument(
"-bert",
"--bert",
action='store_true',
default=False,
help="True if initialize bert embeddings, False if not")
argparser.add_argument(
"-normalize",
"--normalize",
action='store_true',
default=False,
help="True if add normalization across voxels, False if not")
argparser.add_argument("-permutation",
"--permutation",
action='store_true',
default=False,
help="True if permutation, False if not")
argparser.add_argument(
"-permutation_region",
"--permutation_region",
action='store_true',
default=False,
help="True if permutation by brain region, False if not")
### SPECIFY FOR ONE LAYER OR DIFFERENCE IN LAYERS ###
# argparser.add_argument("-across_layer", "--across_layer", help="if across layer depth significance",
# action='store_true', default=False)
### SPECIFY WHICH METRIC ###
argparser.add_argument("-fdr",
"--fdr",
help="if apply FDR",
action='store_true',
default=False)
argparser.add_argument("-llh",
"--llh",
action='store_true',
default=False,
help="True if calculate likelihood, False if not")
argparser.add_argument("-ranking",
"--ranking",
action='store_true',
default=False,
help="True if calculate ranking, False if not")
argparser.add_argument("-rmse",
"--rmse",
action='store_true',
default=False,
help="True if calculate rmse, False if not")
argparser.add_argument("-rsa",
"--rsa",
action='store_true',
default=False,
help="True if calculate rsa, False if not")
argparser.add_argument("-local",
"--local",
action='store_true',
default=False,
help="True if running locally")
### UPDATE FILE PATHS HERE ###
argparser.add_argument(
"--fmri_path",
default="/n/shieber_lab/Lab/users/cjou/fmri/",
type=str,
help="file path to fMRI data on the Odyssey cluster")
argparser.add_argument(
"--to_save_path",
default="/n/shieber_lab/Lab/users/cjou/",
type=str,
help="file path to and create rmse/ranking/llh on the Odyssey cluster")
### UPDATE FILE PATHS HERE ###
args = argparser.parse_args()
if args.num_layers != 12 and args.bert:
print("error: please ensure bert has 12 layers")
exit()
if args.num_layers != 1 and (args.word2vec or args.random
or args.permutation or args.glove):
print("error: please ensure baseline has 1 layer")
exit()
if not args.fdr and not args.llh and not args.ranking and not args.rmse and not args.rsa:
print("error: select at least 1 metric of correlation")
exit()
print("NUMBER OF LAYERS: " + str(args.num_layers))
subjects = [1, 2, 4, 5, 7, 8, 9, 10, 11]
direction, validate, rlabel, elabel, glabel, w2vlabel, bertlabel, plabel, prlabel = helper.generate_labels(
args)
# get subject
if args.local:
volmask = pickle.load(
open(f"../examplesGLM/subj{args.subject_number}/volmask.p", "rb"))
atlas_vals = pickle.load(
open(f"../examplesGLM/subj{args.subject_number}/atlas_vals.p",
"rb"))
atlas_labels = pickle.load(
open(f"../examplesGLM/subj{args.subject_number}/atlas_labels.p",
"rb"))
roi_vals = pickle.load(
open(f"../examplesGLM/subj{args.subject_number}/roi_vals.p", "rb"))
roi_labels = pickle.load(
open(f"../examplesGLM/subj{args.subject_number}/roi_labels.p",
"rb"))
else:
volmask = pickle.load(
open(
str(args.fmri_path) + "subj{args.subject_number}/volmask.p",
"rb"))
atlas_vals = pickle.load(
open(
str(args.fmri_path) + "subj{args.subject_number}/atlas_vals.p",
"rb"))
atlas_labels = pickle.load(
open(
str(args.fmri_path) +
"subj{args.subject_number}/atlas_labels.p", "rb"))
roi_vals = pickle.load(
open(
str(args.fmri_path) + "subj{args.subject_number}/roi_vals.p",
"rb"))
roi_labels = pickle.load(
open(
str(args.fmri_path) + "subj{args.subject_number}/roi_labels.p",
"rb"))
true_roi_labels = helper.compare_labels(roi_labels,
volmask,
subj_num=args.subject_number,
roi=True)
true_atlas_labels = helper.compare_labels(atlas_labels,
volmask,
subj_num=args.subject_number)
# clean labels
final_roi_labels = helper.clean_roi(roi_vals, roi_labels)
final_atlas_labels = helper.clean_atlas(atlas_vals, atlas_labels)
layer_info = []
metric_info = []
roi_info = true_roi_labels * args.num_layers
atlas_info = true_atlas_labels * args.num_layers
# print("ROI INFO: " + str(len(roi_info)))
# print("ATLAS INFO: " + str(len(atlas_info)))
# total=0
# print("ATLAS: " + str(len(final_atlas_labels)))
# for i in range(len(true_atlas_labels)):
# if true_atlas_labels[i] == final_atlas_labels[i]:
# total+=1
# print("TOTAL: " + str(total))
# total=0
# print("ROI: " + str(len(true_roi_labels)))
# for i in range(len(true_roi_labels)):
# if true_roi_labels[i] == final_roi_labels[i]:
# total+=1
# print("TOTAL: " + str(total))
# exit()
# for layer in tqdm(range(1, num_layers+1)):
# file_name = "bertmodel2brain_cv_-subj1-avg_layer" + str(layer)
# values = pickle.load(open("../final_rankings/" + str(file_name) + ".p", "rb"))
# metric_info.extend(values)
# layer_vals = len(values) * [layer]
# layer_info.extend(layer_vals)
# get information
if args.bert:
print("getting metric information per layer...")
for layer in tqdm(range(1, args.num_layers + 1)):
file_name = "bert{}{}-subj{}-{}_layer{}".format(
direction, validate, args.subject_number, args.agg_type, layer)
if args.local:
# values = pickle.load(open("../final_rankings/" + str(file_name) + ".p", "rb"))
# content = scipy.io.loadmat("../final_rankings/layer" + str(layer) + "_ranking_backwards_nifti.mat")
# values = pickle.load(open("../final_rankings/layer" + str(layer) + "_ranking_backwards_nifti.p", "rb"))
if args.ranking:
# values = pickle.load(open("../mat/bertmodel2brain_cv_-subj1-avg_layer" + str(layer) + "-ranking.p", "rb"))
content = scipy.io.loadmat(
"../mat/" + str(file_name) +
"-3dtransform-ranking.mat")["metric"]
if args.rmse:
# bertbrain2model_cv_-subj1-avg_layer1-3dtransform-rmse.mat
content = scipy.io.loadmat(
"../mat/" + str(file_name) +
"-3dtransform-rmse.mat")["metric"]
if args.llh:
content = np.abs(
scipy.io.loadmat("../mat/" + str(file_name) +
"-3dtransform-llh.mat")["metric"])
if args.rsa:
content = scipy.io.loadmat(
"../mat/" + str(file_name) +
"-3dtransform-rsa.mat")["metric"]
values = helper.convert_matlab_to_np(content, volmask)
else:
values = pickle.load(
open(
str(args.to_save_path) + "final_rankings/" +
str(file_name) + ".p", "rb"))
metric_info.extend(values)
layer_vals = len(values) * [layer]
layer_info.extend(layer_vals)
to_save_file = str(plabel) + str(prlabel) + str(glabel) + str(
w2vlabel) + str(bertlabel) + str(direction) + str(
validate) + "-subj" + str(args.subject_number) + "-bert"
elif not args.glove and not args.word2vec:
for layer in tqdm(range(1, args.num_layers + 1)):
file_name = "{}{}-subj{}-parallel-english-to-{}-model-{}layer-brnn-pred-layer{}-{}-3dtransform-".format(
direction, validate, args.subject_number, args.language,
args.num_layers, layer, args.agg_type)
print(file_name)
if args.local:
if args.ranking:
content = scipy.io.loadmat("../mat/" + file_name +
"ranking.mat")["metric"]
if args.rmse:
content = scipy.io.loadmat("../mat/" + file_name +
"rmse.mat")["metric"]
if args.llh:
content = np.abs(
scipy.io.loadmat("../mat/" + file_name +
"llh.mat")["metric"])
values = helper.convert_matlab_to_np(content, volmask)
else:
values = pickle.load(
open(
str(args.to_save_path) + "final_rankings/" +
str(file_name) + ".p", "rb"))
metric_info.extend(values)
layer_vals = len(values) * [layer]
layer_info.extend(layer_vals)
to_save_file = "{}_{}_subj{}_{}layer_{}".format(
direction, validate, args.subject_number, args.num_layers,
args.language)
else: # word2vec, glove
pass
print("LAYER INFO: " + str(len(layer_info)))
print("METRIC INFO: " + str(len(metric_info)))
if args.ranking and args.model_to_brain:
df_dict = {
'layer': layer_info,
'AR': metric_info,
'ROI': roi_info,
'atlas': atlas_info
}
df = pd.DataFrame(df_dict)
df_slice = df.loc[df["layer"] == 1][["atlas", "AR"]]
avg_df = df_slice.groupby(['atlas']).mean()
print(avg_df.sort_values(by='AR', ascending=False).head())
print("plotting values...")
helper.plot_roi_across_layers(
df, "AR", "../fixed_roi_ar_" + to_save_file + ".png")
helper.plot_atlas_across_layers(
df, "AR", "../fixed_atlas_ar_" + to_save_file + ".png")
if args.rmse:
df_dict = {
'layer': layer_info,
'RMSE': metric_info,
'ROI': roi_info,
'atlas': atlas_info
}
df = pd.DataFrame(df_dict)
df_slice = df.loc[df["layer"] == 1][["atlas", "RMSE"]]
avg_df = df_slice.groupby(['atlas']).mean()
print(avg_df.sort_values(by='RMSE', ascending=True).head())
print("plotting values...")
helper.plot_roi_across_layers(
df, "RMSE", "../fixed_roi_rmse_" + to_save_file + ".png")
helper.plot_atlas_across_layers(
df, "RMSE", "../fixed_atlas_rmse_" + to_save_file + ".png")
if args.llh:
df_dict = {
'layer': layer_info,
'LLH': metric_info,
'ROI': roi_info,
'atlas': atlas_info
}
df = pd.DataFrame(df_dict)
df_slice = df.loc[df["layer"] == 1][["atlas", "LLH"]]
avg_df = df_slice.groupby(['atlas']).mean()
print(avg_df.sort_values(by='LLH', ascending=True).head())
print("plotting values...")
helper.plot_roi_across_layers(
df, "LLH", "../fixed_roi_llh_" + to_save_file + ".png")
helper.plot_atlas_across_layers(
df, "LLH", "../fixed_atlas_llh_" + to_save_file + ".png")
if args.rsa:
df_dict = {
'layer': layer_info,
'correlation_coefficient': metric_info,
'ROI': roi_info,
'atlas': atlas_info
}
df = pd.DataFrame(df_dict)
print("ATLAS...")
df_slice = df.loc[df["layer"] == 1][[
"atlas", "correlation_coefficient"
]]
avg_df = df_slice.groupby(['atlas']).mean()
print(
avg_df.sort_values(by='correlation_coefficient',
ascending=True).head())
print(
avg_df.sort_values(by='correlation_coefficient',
ascending=False).head())
print("ROI...")
df_slice = df.loc[df["layer"] == 1][["ROI", "correlation_coefficient"]]
avg_df = df_slice.groupby(['ROI']).mean()
print(
avg_df.sort_values(by='correlation_coefficient',
ascending=True).head())
print(
avg_df.sort_values(by='correlation_coefficient',
ascending=False).head())
print("plotting values...")
helper.plot_roi_across_layers(
df, "correlation_coefficient",
"../fixed_roi_rsa_" + to_save_file + ".png")
helper.plot_atlas_across_layers(
df, "correlation_coefficient",
"../fixed_atlas_rsa_" + to_save_file + ".png")
print("done.")
return
def generate_file_name(args, subject_number, which_layer):
direction, validate, rlabel, elabel, glabel, w2vlabel, bertlabel, plabel, prlabel = helper.generate_labels(
args)
if args.bert or args.word2vec or args.glove:
specific_file = str(plabel) + str(prlabel) + str(rlabel) + str(
elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(
direction) + str(validate) + "-subj{}-{}_layer{}"
file_name = specific_file.format(subject_number, args.agg_type,
which_layer)
else:
specific_file = str(plabel) + str(prlabel) + str(rlabel) + str(
elabel
) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(direction) + str(
validate
) + "-subj{}-parallel-english-to-{}-model-{}layer-{}-pred-layer{}-{}"
file_name = specific_file.format(subject_number, args.language,
args.num_layers, args.model_type,
which_layer, args.agg_type)
return file_name
def main():
############# GET ARGUMENTS BELOW #############
parser = argparse.ArgumentParser(description="entire OpenNMT pipeline: data prep, model, decoding, visualization")
# model type
parser.add_argument("-language", "--language", help="Target language ('spanish', 'german', 'italian', 'french', 'swedish')", type=str, default='spanish')
parser.add_argument("-num_layers", "--num_layers", help="Total number of layers ('2', '4')", type=int, default=2)
parser.add_argument("-model_type", "--model_type", help="Type of model ('brnn', 'rnn')", type=str, default='brnn')
parser.add_argument("-which_layer", "--which_layer", help="Layer of interest in [1: total number of layers]", type=int, default=1)
parser.add_argument("-agg_type", "--agg_type", help="Aggregation type ('avg', 'max', 'min', 'last')", type=str, default='avg')
parser.add_argument("-nbatches", "--nbatches", help="Total number of batches to run", type=int, default=100)
# subject for brain data
parser.add_argument("-subj_num", "--subj_num", help="fMRI subject number ([1:11])", type=int, default=1)
parser.add_argument("-format_data", "--format_data", help="Format fMRI data", action='store_true', default=False)
# opennmt model
parser.add_argument("-create_model", "--create_model", help="create OpenNMT prediction model", action='store_true', default=False)
# initializations
parser.add_argument("-random", "--random", action='store_true', default=False, help="True if add cross validation, False if not")
parser.add_argument("-rand_embed", "--rand_embed", action='store_true', default=False, help="True if initialize random embeddings, False if not")
parser.add_argument("-glove", "--glove", action='store_true', default=False, help="True if initialize glove embeddings, False if not")
parser.add_argument("-word2vec", "--word2vec", action='store_true', default=False, help="True if initialize word2vec embeddings, False if not")
parser.add_argument("-bert", "--bert", action='store_true', default=False, help="True if initialize bert embeddings, False if not")
parser.add_argument("-permutation", "--permutation", action='store_true', default=False, help="True if permutation, False if not")
parser.add_argument("-permutation_region", "--permutation_region", action='store_true', default=False, help="True if permutation by brain region, False if not")
# evaluation metrics
parser.add_argument("-decoding", "--decoding", action='store_true', default=False, help="True if decoding, False if not")
parser.add_argument("-cross_validation", "--cross_validation", help="Add flag if add cross validation", action='store_true', default=False)
parser.add_argument("-brain_to_model", "--brain_to_model", help="Add flag if regressing brain to model", action='store_true', default=False)
parser.add_argument("-model_to_brain", "--model_to_brain", help="Add flag if regressing model to brain", action='store_true', default=False)
parser.add_argument("-fdr", "--fdr", action='store_true', default=False, help="True if FDR, False if not")
parser.add_argument("-rank", "--rank", action='store_true', default=False, help="True if rank, False if not")
parser.add_argument("-llh", "--llh", action='store_true', default=False, help="True if likelihood, False if not")
parser.add_argument("-local", "--local", action='store_true', default=False, help="True if running locally, False if not")
args = parser.parse_args()
############# VALIDATE ARGUMENTS #############
helper.validate_arguments()
############# CREATE LABELS #############
direction, validate, rlabel, elabel, glabel, w2vlabel, bertlabel, plabel, prlabel = helper.generate_labels(args)
############# GLOBAL OPTIONS #############
get_residuals_and_make_scripts, options = helper.generate_options(args)
############# CREATE OPENNMT MODEL #############
if args.create_model:
### multiparallelize texts
### todo: add here
### preprocess ### train ### translate
### todo: add locations
training_src = ""
training_tgt = ""
validation_src = ""
validation_tgt = ""
preprocess = "python preprocess.py -train_src ../multiparallelize/training/" + str(training_text) +
" -train_tgt ../multiparallelize/training/" + str(training_tgt) +
" -valid_src ../multiparallelize/validation/" + str(validation_src) +
" -valid_tgt ../multiparallelize/training/validation/" + str(validation_tgt) +
" -save ../multiparallelize"
os.system(preprocess)
train = "python train.py -data data/english-to-spanish -save_model small-english-to-spanish-model -gpu 0 -separate_layers"
os.system(train)
translate = "python translate.py -model ../final_models/english-to-spanish-model_acc_61.26_ppl_6.28_e13.pt -src cleaned_sentencesGLM.txt -output ../predictions/english-to-spanish-model-pred.txt -replace_unk -verbose -dump_layers ../predictions/english-to-spanish-model-pred.pt"
os.system(translate)
def main():
argparser = argparse.ArgumentParser(description="layer and subject group level comparison")
argparser.add_argument("-subject_number", "--subject_number", type=int, default=1,
help="subject number (fMRI data) for decoding")
### SPECIFY MODEL PARAMETERS ###
argparser.add_argument("-cross_validation", "--cross_validation", help="Add flag if add cross validation",
action='store_true', default=True)
argparser.add_argument("-brain_to_model", "--brain_to_model", help="Add flag if regressing brain to model",
action='store_true', default=False)
argparser.add_argument("-model_to_brain", "--model_to_brain", help="Add flag if regressing model to brain",
action='store_true', default=True)
argparser.add_argument("-agg_type", "--agg_type", help="Aggregation type ('avg', 'max', 'min', 'last')", type=str,
default='avg')
argparser.add_argument("-language", "--language",
help="Target language ('spanish', 'german', 'italian', 'french', 'swedish')", type=str,
default='spanish')
argparser.add_argument("-num_layers", "--num_layers", help="Total number of layers ('2', '4')", type=int, required=True)
argparser.add_argument("-random", "--random", action='store_true', default=False,
help="True if initialize random brain activations, False if not")
argparser.add_argument("-rand_embed", "--rand_embed", action='store_true', default=False,
help="True if initialize random embeddings, False if not")
argparser.add_argument("-glove", "--glove", action='store_true', default=False,
help="True if initialize glove embeddings, False if not")
argparser.add_argument("-word2vec", "--word2vec", action='store_true', default=False,
help="True if initialize word2vec embeddings, False if not")
argparser.add_argument("-bert", "--bert", action='store_true', default=True,
help="True if initialize bert embeddings, False if not")
argparser.add_argument("-normalize", "--normalize", action='store_true', default=False,
help="True if add normalization across voxels, False if not")
argparser.add_argument("-permutation", "--permutation", action='store_true', default=False,
help="True if permutation, False if not")
argparser.add_argument("-permutation_region", "--permutation_region", action='store_true', default=False,
help="True if permutation by brain region, False if not")
### PLOTTING ###
argparser.add_argument("-which_layer", "--which_layer", help="Layer of interest in [1: total number of layers]",
type=int, default=1)
### SPECIFY FOR SINGLE SUBJECT OR GROUP LEVEL ANALYSIS ###
argparser.add_argument("-single_subject", "--single_subject", help="if single subject analysis",
action='store_true', default=False)
argparser.add_argument("-group_level", "--group_level", help="if group level analysis", action='store_true',
default=False)
argparser.add_argument("-searchlight", "--searchlight", help="if searchlight", action='store_true', default=False)
### SPECIFY FOR ONE LAYER OR DIFFERENCE IN LAYERS ###
argparser.add_argument("-single_layer", "--single_layer", help="if single layer significance",
action='store_true', default=False)
argparser.add_argument("-across_layer", "--across_layer", help="if across layer depth significance",
action='store_true', default=False)
### SPECIFY WHICH METRIC ###
argparser.add_argument("-fdr", "--fdr", help="if apply FDR", action='store_true', default=False)
argparser.add_argument("-llh", "--llh", action='store_true', default=False,
help="True if calculate likelihood, False if not")
argparser.add_argument("-ranking", "--ranking", action='store_true', default=False,
help="True if calculate ranking, False if not")
argparser.add_argument("-rmse", "--rmse", action='store_true', default=False,
help="True if calculate rmse, False if not")
argparser.add_argument("-rsa", "--rsa", action='store_true', default=False,
help="True if calculate rsa, False if not")
argparser.add_argument("-nested", "--nested", action='store_true', default=True, help="True if running nested")
argparser.add_argument("-local", "--local", action='store_true', default=False, help="True if running locally")
argparser.add_argument("-save_by_voxel", "--save_by_voxel", action='store_true', default=False, help="True if save by voxel")
argparser.add_argument("-compare_models", "--compare_models", action='store_true', default=False, help="True if compare models")
args = argparser.parse_args()
if args.num_layers != 12 and args.bert:
print("error: please ensure bert has 12 layers")
exit()
if args.num_layers != 1 and (args.word2vec or args.random or args.permutation or args.glove):
print("error: please ensure baseline has 1 layerc")
exit()
if not args.fdr and not args.llh and not args.ranking and not args.rmse:
print("error: select at least 1 metric of correlation")
exit()
print("NUMBER OF LAYERS: " + str(args.num_layers))
subjects = [1,2,4,5,7,8,9,10,11]
print("getting common brain space")
common_space = helper.load_common_space(subjects, local=args.local)
voxel_coordinates = np.transpose(np.nonzero(common_space))
print(voxel_coordinates.shape)
direction, validate, rlabel, elabel, glabel, w2vlabel, bertlabel, plabel, prlabel = helper.generate_labels(args)
if args.fdr:
metric = "fdr"
if args.rmse:
metric = "rmse"
if args.rsa:
metric = "rsa"
if args.ranking:
metric = "ranking"
if args.llh:
metric = "llh"
if args.single_subject and args.across_layer:
first = True
for layer_num in list(range(1, args.num_layers + 1)):
print("generating file names...")
layer_file_name = generate_file_name(args, args.subject_number, layer_num)
print("retrieving file contents...")
layer, pvals = get_file(args, args.subject_number, layer_file_name)
if first:
updated_brain = layer
best_layer = layer.astype(bool) * layer_num
first = False
mask = layer.astype(bool)
else:
if args.llh or args.ranking:
max_vals = np.maximum(updated_brain, layer)
# temp = np.minimum(updated_brain, layer)
# print("SAME: " + str(np.sum(np.equal(max_vals, temp).astype(bool) * mask)))
elif args.rmse:
max_vals = np.minimum(updated_brain, layer)
else:
print("select llh, ranking, or rmse")
exit()
from_layer = np.equal(max_vals, layer).astype(bool) * mask * layer_num
temp_best_layer = np.equal(max_vals, updated_brain).astype(bool) * mask * best_layer
best_layer = np.maximum(from_layer, temp_best_layer)
# print("NEW: " + str(np.sum(from_layer.astype(bool))))
# print("OLD: " + str(np.sum(temp_best_layer.astype(bool))))
updated_brain = max_vals
if args.bert:
file_name = "bert{}{}subj{}_{}".format(
direction,
validate,
args.subject_number,
args.agg_type,
)
else:
specific_file = str(plabel) + str(prlabel) + str(rlabel) + str(elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(direction) + str(validate) + "-subj{}-parallel-english-to-{}-model-{}layer-{}-pred-layer-{}"
file_name = specific_file.format(
args.subject_number,
args.language,
args.num_layers,
"brnn",
args.agg_type
)
print("BEST LAYER")
total = 0
for layer in range(1, args.num_layers +1):
print("LAYER" + str(layer))
print(np.sum(best_layer == layer))
total += np.sum(best_layer == layer)
print("TOTAL:" + str(total))
scipy.io.savemat("../" + str(file_name) + "_best_" + str(metric) + ".mat", dict(metric = best_layer.astype(np.int16)))
if args.save_by_voxel:
per_layer = []
for layer_num in tqdm(list(range(1, args.num_layers + 1))):
per_subject = []
for subj_num in subjects:
layer_file_name = generate_file_name(args, subj_num, layer_num)
layer, _ = get_file(args, subj_num, layer_file_name)
voxel_values = layer[np.nonzero(common_space)]
# print("LENGTH: " + str(len(voxel_values)))
per_subject.append(voxel_values)
print("PER LAYER: ")
print(np.array(per_subject).shape)
per_layer.append(0.5 * np.transpose(np.array(per_subject)))
print(np.array(per_layer).shape)
print("BEFORE BASELINE")
print(np.array(per_layer).shape)
# add other embeddings
if args.compare_models:
for options in [[True, False], [False, True], [False, False]]:
a,b = options
if a == False and b == False:
nmt_layers = 4
else:
nmt_layers = 1
for layer_num in tqdm(list(range(1, nmt_layers + 1))):
per_subject = []
a, b = options
for subj_num in subjects:
layer_file_name = generate_file_name(args, subj_num, layer_num, baseline=True, glove=a, word2vec=b)
layer, _ = get_file(args, subj_num, layer_file_name)
voxel_values = layer[np.nonzero(common_space)]
per_subject.append(voxel_values)
per_layer.append(0.5 * np.transpose(np.array(per_subject)))
print("AFTER BASELINE")
print(np.array(per_layer).shape)
print("AT THE END: ")
print(np.array(per_layer).shape)
per_voxel = np.stack( per_layer, axis=-1 )
print(per_voxel.shape)
print(per_voxel[0].shape)
print(per_voxel[0])
scipy.io.savemat("../mfit/nested_all_best_" + str(metric) + "_by_voxel.mat", dict(metric = per_voxel.astype(np.float32)))
print("done.")
return
def main():
# parse arguments
argparser = argparse.ArgumentParser(description="plot RMSE on 3d brain")
# argparser.add_argument('--rmse', type=str, help="Location of RMSE for entire brain (.p)", required=True)
argparser.add_argument(
"-language",
"--language",
help=
"Target language ('spanish', 'german', 'italian', 'french', 'swedish')",
type=str,
default='spanish')
argparser.add_argument("-num_layers",
"--num_layers",
help="Total number of layers ('2', '4')",
type=int,
default=2)
argparser.add_argument("-model_type",
"--model_type",
help="Type of model ('brnn', 'rnn')",
type=str,
default='brnn')
argparser.add_argument(
"-which_layer",
"--which_layer",
help="Layer of interest in [1: total number of layers]",
type=int,
default=1)
argparser.add_argument(
"-agg_type",
"--agg_type",
help="Aggregation type ('avg', 'max', 'min', 'last')",
type=str,
default='avg')
argparser.add_argument("-subject_number",
"--subject_number",
type=int,
default=1,
help="subject number (fMRI data) for decoding")
argparser.add_argument("-cross_validation",
"--cross_validation",
help="Add flag if add cross validation",
action='store_true',
default=False)
argparser.add_argument("-brain_to_model",
"--brain_to_model",
help="Add flag if regressing brain to model",
action='store_true',
default=False)
argparser.add_argument("-model_to_brain",
"--model_to_brain",
help="Add flag if regressing model to brain",
action='store_true',
default=False)
argparser.add_argument(
"-glove",
"--glove",
action='store_true',
default=False,
help="True if initialize glove embeddings, False if not")
argparser.add_argument(
"-word2vec",
"--word2vec",
action='store_true',
default=False,
help="True if initialize word2vec embeddings, False if not")
argparser.add_argument(
"-bert",
"--bert",
action='store_true',
default=False,
help="True if initialize bert embeddings, False if not")
argparser.add_argument(
"-rand_embed",
"--rand_embed",
action='store_true',
default=False,
help="True if initialize random embeddings, False if not")
argparser.add_argument("-random",
"--random",
action='store_true',
default=False,
help="True if add cross validation, False if not")
argparser.add_argument("-permutation",
"--permutation",
action='store_true',
default=False,
help="True if permutation, False if not")
argparser.add_argument(
"-permutation_region",
"--permutation_region",
action='store_true',
default=False,
help="True if permutation by brain region, False if not")
args = argparser.parse_args()
print("getting arguments...")
# rmses = args.rmse
# file_name = rmses.split("/")[-1].split(".")[0]
# get residuals
# check conditions // can remove when making pipeline
if args.brain_to_model and args.model_to_brain:
print("select only one flag for brain_to_model or model_to_brain")
exit()
if not args.brain_to_model and not args.model_to_brain:
print("select at least flag for brain_to_model or model_to_brain")
exit()
direction, validate, rlabel, elabel, glabel, w2vlabel, bertlabel, plabel, prlabel = helper.generate_labels(
args)
# residual_file = sys.argv[1]
if not args.word2vec and not args.glove and not args.bert and not args.random:
specific_file = str(plabel) + str(prlabel) + str(rlabel) + str(
elabel
) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(direction) + str(
validate
) + "-subj{}-parallel-english-to-{}-model-{}layer-{}-pred-layer{}-{}"
file_name = specific_file.format(args.subject_number, args.language,
args.num_layers, args.model_type,
args.which_layer, args.agg_type)
else:
file_name = str(plabel) + str(prlabel) + str(rlabel) + str(
elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(
direction) + str(validate) + "-subj{}-{}_layer{}".format(
args.subject_number, args.agg_type, args.which_layer)
# file_loc = str(plabel) + str(prlabel) + str(rlabel) + str(elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(direction) + str(validate) + "subj{}_parallel-english-to-{}-model-{}layer-{}-pred-layer{}-{}"
# file_name = file_loc.format(
# args.subject_number,
# args.language,
# args.num_layers,
# args.model_type,
# args.which_layer,
# args.agg_type
# )
residual_file = "../rmses/concatenated-" + str(file_name) + ".p"
# file_name = residual_file.split("/")[-1].split(".")[0]
data = pickle.load(open(residual_file, "rb"))
# get volmask
subject_number = args.subject_number
file_path = "../examplesGLM/subj{}/volmask.p".format(subject_number)
volmask = pickle.load(open(file_path, "rb"))
if not os.path.exists('../3d-brain/'):
os.makedirs('../3d-brain/')
print("transforming coordinates...")
transform_data = helper.transform_coordinates(data,
volmask,
save_path="../3d-brain/" +
file_name,
metric="rmse")
print("ORIGINAL DATA: " + str(len(data)))
print("TRANSFORMED DATA: " + str(transform_data.shape))
print("plotting data...")
f_name = "../3d-brain/" + file_name + "-glass-brain.png"
plot_on_glass(transform_data, f_name)
# plot_interactive(transform_data, file_name)
# plot_roi(transform_data, file_name)
print('done.')
return
def main():
# parse arguments
argparser = argparse.ArgumentParser(
description="transform coordinates for plotting")
argparser.add_argument(
"-language",
"--language",
help=
"Target language ('spanish', 'german', 'italian', 'french', 'swedish')",
type=str,
default='spanish')
argparser.add_argument("-num_layers",
"--num_layers",
help="Total number of layers ('2', '4')",
type=int,
default=2)
argparser.add_argument("-model_type",
"--model_type",
help="Type of model ('brnn', 'rnn')",
type=str,
default='brnn')
argparser.add_argument(
"-which_layer",
"--which_layer",
help="Layer of interest in [1: total number of layers]",
type=int,
default=1)
argparser.add_argument(
"-agg_type",
"--agg_type",
help="Aggregation type ('avg', 'max', 'min', 'last')",
type=str,
default='avg')
argparser.add_argument("-subject_number",
"--subject_number",
type=int,
default=1,
help="subject number (fMRI data) for decoding")
argparser.add_argument("-cross_validation",
"--cross_validation",
help="Add flag if add cross validation",
action='store_true',
default=False)
argparser.add_argument("-brain_to_model",
"--brain_to_model",
help="Add flag if regressing brain to model",
action='store_true',
default=False)
argparser.add_argument("-model_to_brain",
"--model_to_brain",
help="Add flag if regressing model to brain",
action='store_true',
default=False)
argparser.add_argument(
"-glove",
"--glove",
action='store_true',
default=False,
help="True if initialize glove embeddings, False if not")
argparser.add_argument(
"-word2vec",
"--word2vec",
action='store_true',
default=False,
help="True if initialize word2vec embeddings, False if not")
argparser.add_argument(
"-bert",
"--bert",
action='store_true',
default=False,
help="True if initialize bert embeddings, False if not")
argparser.add_argument(
"-rand_embed",
"--rand_embed",
action='store_true',
default=False,
help="True if initialize random embeddings, False if not")
argparser.add_argument("-random",
"--random",
action='store_true',
default=False,
help="True if add cross validation, False if not")
argparser.add_argument("-permutation",
"--permutation",
action='store_true',
default=False,
help="True if permutation, False if not")
argparser.add_argument(
"-permutation_region",
"--permutation_region",
action='store_true',
default=False,
help="True if permutation by brain region, False if not")
# metrics
argparser.add_argument("-rmse",
"--rmse",
action='store_true',
default=False,
help="True if rmse, False if not")
argparser.add_argument("-fdr",
"--fdr",
action='store_true',
default=False,
help="True if fdr, False if not")
argparser.add_argument("-rank",
"--rank",
action='store_true',
default=False,
help="True if rank, False if not")
### UPDATE FILE PATHS HERE ###
argparser.add_argument(
"--fmri_path",
default="/n/shieber_lab/Lab/users/cjou/fmri/",
type=str,
help="file path to fMRI data on the Odyssey cluster")
argparser.add_argument(
"--to_save_path",
default="/n/shieber_lab/Lab/users/cjou/",
type=str,
help="file path to and create rmse/ranking/llh on the Odyssey cluster")
### UPDATE FILE PATHS HERE ###
args = argparser.parse_args()
# verify arguments
if args.rmse and args.fdr and args.rank:
print("select only one flag for rmse, fdr, or rank")
exit()
if not args.rmse and not args.fdr and not args.rank:
print("select at least flag for rmse, fdr, or rank")
exit()
print("getting arguments...")
direction, validate, rlabel, elabel, glabel, w2vlabel, bertlabel, plabel, prlabel = helper.generate_labels(
args)
file_loc = str(plabel) + str(prlabel) + str(rlabel) + str(elabel) + str(
glabel) + str(w2vlabel) + str(bertlabel) + str(direction) + str(
validate
) + "subj{}_parallel-english-to-{}-model-{}layer-{}-pred-layer{}-{}"
file_name = file_loc.format(args.subject_number, args.language,
args.num_layers, args.model_type,
args.which_layer, args.agg_type)
if not os.path.exists(str(args.to_save_path) + '3d-brain/'):
os.makedirs(str(args.to_save_path) + '3d-brain/')
save_location = str(args.to_save_path) + '3d-brain/'
# set save location
if args.rmse:
# TODO
open_location = str(
args.to_save_path) + "rmse/" + str(file_name) + "_subj" + str(
args.subject_number)
metric = "rmse"
elif args.fdr:
open_location = str(
args.to_save_path) + "fdr/" + str(file_name) + "_subj" + str(
args.subject_number)
metric = "fdr"
points = pickle.load(
open(open_location + "_valid_correlations_2d_coordinates.p", "rb"))
elif args.rank:
# TODO
open_location = str(args.to_save_path) + "rankings_od32/" + str(
file_name) + "_subj" + str(args.subject_number)
metric = "rank"
else:
print("error")
exit()
# get volmask
file_path = str(args.fmri_path) + "examplesGLM/subj{}/volmask.p".format(
args.subject_number)
volmask = pickle.load(open(file_path, "rb"))
# transform coordinates and save
print("METRIC: " + str(metric))
_ = helper.transform_coordinates(points,
volmask,
save_location=save_location,
metric=metric)
print("done.")
def main():
argparser = argparse.ArgumentParser(description="calculate rankings for model-to-brain")
argparser.add_argument("-language", "--language", help="Target language ('spanish', 'german', 'italian', 'french', 'swedish')", type=str, default='spanish')
argparser.add_argument("-num_layers", "--num_layers", help="Total number of layers ('2', '4')", type=int, default=12)
argparser.add_argument("-model_type", "--model_type", help="Type of model ('brnn', 'rnn')", type=str, default='brnn')
argparser.add_argument("-agg_type", "--agg_type", help="Aggregation type ('avg', 'max', 'min', 'last')", type=str, default='avg')
argparser.add_argument("-subject_number", "--subject_number", help="fMRI subject number ([1:11])", type=int, default=1)
argparser.add_argument("-cross_validation", "--cross_validation", help="Add flag if add cross validation", action='store_true', default=True)
argparser.add_argument("-brain_to_model", "--brain_to_model", help="Add flag if regressing brain to model", action='store_true', default=False)
argparser.add_argument("-model_to_brain", "--model_to_brain", help="Add flag if regressing model to brain", action='store_true', default=False)
argparser.add_argument("-glove", "--glove", action='store_true', default=False, help="True if initialize glove embeddings, False if not")
argparser.add_argument("-word2vec", "--word2vec", action='store_true', default=False, help="True if initialize word2vec embeddings, False if not")
argparser.add_argument("-bert", "--bert", action='store_true', default=False, help="True if initialize bert embeddings, False if not")
argparser.add_argument("-rand_embed", "--rand_embed", action='store_true', default=False, help="True if initialize random embeddings, False if not")
argparser.add_argument("-random", "--random", action='store_true', default=False, help="True if add cross validation, False if not")
argparser.add_argument("-permutation", "--permutation", action='store_true', default=False, help="True if permutation, False if not")
argparser.add_argument("-permutation_region", "--permutation_region", action='store_true', default=False, help="True if permutation by brain region, False if not")
argparser.add_argument("-normalize", "--normalize", action='store_true', default=False, help="True if add normalization across voxels, False if not")
argparser.add_argument("-local", "--local", action='store_true', default=False, help="True if local, False if not")
argparser.add_argument("-log", "--log", action='store_true', default=False, help="True if use log coordinates, False if not")
argparser.add_argument("-rmse", "--rmse", action='store_true', default=False, help="True if rmse, False if not")
argparser.add_argument("-ranking", "--ranking", action='store_true', default=False, help="True if ranking, False if not")
argparser.add_argument("-fdr", "--fdr", action='store_true', default=False, help="True if fdr, False if not")
argparser.add_argument("-llh", "--llh", action='store_true', default=False, help="True if llh, False if not")
argparser.add_argument("-rsa", "--rsa", action='store_true', default=False, help="True if rsa, False if not")
argparser.add_argument("-alpha", "--alpha", action='store_true', default=False, help="True if alpha, False if not")
argparser.add_argument("-total_batches", "--total_batches", type=int, help="total number of batches residual_name is spread across", default=100)
### UPDATE FILE PATHS HERE ###
argparser.add_argument("--fmri_path", default="/n/shieber_lab/Lab/users/cjou/fmri/", type=str, help="file path to fMRI data on the Odyssey cluster")
argparser.add_argument("--to_save_path", default="/n/shieber_lab/Lab/users/cjou/", type=str, help="file path to and create rmse/ranking/llh on the Odyssey cluster")
### UPDATE FILE PATHS HERE ###
args = argparser.parse_args()
# check conditions // can remove when making pipeline
if args.brain_to_model and args.model_to_brain:
print("select only one flag for brain_to_model or model_to_brain")
exit()
if (not args.brain_to_model and not args.model_to_brain) and not args.rsa:
print("select at least flag for brain_to_model or model_to_brain // or rsa")
exit()
# if not args.rmse and not args.ranking and not args.fdr and not args.llh and not args.rsa:
# print("select at least flag for rmse, ranking, fdr, llh, rsa")
# exit()
print("getting volmask...")
direction, validate, rlabel, elabel, glabel, w2vlabel, bertlabel, plabel, prlabel = helper.generate_labels(args)
print("CROSS VALIDATION: " + str(args.cross_validation))
print("BRAIN_TO_MODEL: " + str(args.brain_to_model))
print("MODEL_TO_BRAIN: " + str(args.model_to_brain))
print("GLOVE: " + str(args.glove))
print("WORD2VEC: " + str(args.word2vec))
print("BERT: " + str(args.bert))
print("RANDOM BRAIN: " + str(args.random))
print("RANDOM EMBEDDINGS: " + str(args.rand_embed))
print("PERMUTATION: " + str(args.permutation))
print("PERMUTATION REGION: " + str(args.permutation_region))
if args.local:
volmask = pickle.load( open( f"../examplesGLM/subj{args.subject_number}/volmask.p", "rb" ) )
if args.ranking:
atlas_vals = pickle.load( open( f"../examplesGLM/subj{args.subject_number}/atlas_vals.p", "rb" ) )
atlas_labels = pickle.load( open( f"../examplesGLM/subj{args.subject_number}/atlas_labels.p", "rb" ) )
roi_vals = pickle.load( open( f"../examplesGLM/subj{args.subject_number}/roi_vals.p", "rb" ) )
roi_labels = pickle.load( open( f"../examplesGLM/subj{args.subject_number}/roi_labels.p", "rb" ) )
else:
volmask = pickle.load( open( "{}subj{}/volmask.p".format(args,fmri_path, args.subject_number), "rb" ) )
if args.ranking:
atlas_vals = pickle.load( open( "{}subj{}/atlas_vals.p".format(args,fmri_path, args.subject_number), "rb" ) )
atlas_labels = pickle.load( open( "{}subj{}/atlas_labels.p".format(args,fmri_path, args.subject_number), "rb" ) )
roi_vals = pickle.load( open( "{}subj{}/roi_vals.p".format(args,fmri_path, args.subject_number), "rb" ) )
roi_labels = pickle.load( open( "{}subj{}/roi_labels.p".format(args,fmri_path, args.subject_number), "rb" ) )
### MAKE PATHS ###
print("making paths...")
if not os.path.exists('../mat/'):
os.makedirs('../mat/')
if args.brain_to_model:
metrics = ["rmse", "llh"]
elif args.rsa:
metrics = ["rsa"]
elif args.alpha:
metrics = ["alpha"]
else:
metrics = ["ranking", "rmse", "llh"]
for layer in tqdm(range(1, args.num_layers+1)):
print("LAYER: " + str(layer))
for metric in metrics:
print("METRIC: " + str(metric))
if args.bert or args.word2vec or args.glove:
specific_file = str(plabel) + str(prlabel) + str(rlabel) + str(elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(direction) + str(validate) + "-subj{}-{}_layer{}"
file_name = specific_file.format(
args.subject_number,
args.agg_type,
layer
)
else:
specific_file = str(plabel) + str(prlabel) + str(rlabel) + str(elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(direction) + str(validate) + "-subj{}-parallel-english-to-{}-model-{}layer-{}-pred-layer{}-{}"
file_name = specific_file.format(
args.subject_number,
args.language,
args.num_layers,
args.model_type,
layer,
args.agg_type
)
print("transform coordinates...")
if not args.word2vec and not args.glove and not args.bert and not args.random:
specific_file = str(plabel) + str(prlabel) + str(rlabel) + str(elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(direction) + str(validate) + "-subj{}-parallel-english-to-{}-model-{}layer-{}-pred-layer{}-{}"
file_format = specific_file.format(
args.subject_number,
args.language,
args.num_layers,
args.model_type,
layer,
args.agg_type
)
else:
file_format = str(plabel) + str(prlabel) + str(rlabel) + str(elabel) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(direction) + str(validate) + "-subj{}-{}_layer{}".format(args.subject_number, args.agg_type, layer)
final_values = concatenate_all(file_format, args, metric)
_ = helper.transform_coordinates(final_values, volmask, save_path="../mat/" + file_name, metric=metric)
print('done.')
def main():
argparser = argparse.ArgumentParser(
description="layer and subject group level comparison")
argparser.add_argument("-subject_number",
"--subject_number",
type=int,
default=1,
help="subject number (fMRI data) for decoding")
### SPECIFY MODEL PARAMETERS ###
argparser.add_argument("-cross_validation",
"--cross_validation",
help="Add flag if add cross validation",
action='store_true',
default=False)
argparser.add_argument("-brain_to_model",
"--brain_to_model",
help="Add flag if regressing brain to model",
action='store_true',
default=False)
argparser.add_argument("-model_to_brain",
"--model_to_brain",
help="Add flag if regressing model to brain",
action='store_true',
default=False)
argparser.add_argument(
"-agg_type",
"--agg_type",
help="Aggregation type ('avg', 'max', 'min', 'last')",
type=str,
default='avg')
argparser.add_argument(
"-language",
"--language",
help=
"Target language ('spanish', 'german', 'italian', 'french', 'swedish')",
type=str,
default='spanish')
argparser.add_argument("-num_layers",
"--num_layers",
help="Total number of layers ('2', '4')",
type=int,
required=True)
argparser.add_argument(
"-random",
"--random",
action='store_true',
default=False,
help="True if initialize random brain activations, False if not")
argparser.add_argument(
"-rand_embed",
"--rand_embed",
action='store_true',
default=False,
help="True if initialize random embeddings, False if not")
argparser.add_argument(
"-glove",
"--glove",
action='store_true',
default=False,
help="True if initialize glove embeddings, False if not")
argparser.add_argument(
"-word2vec",
"--word2vec",
action='store_true',
default=False,
help="True if initialize word2vec embeddings, False if not")
argparser.add_argument(
"-bert",
"--bert",
action='store_true',
default=False,
help="True if initialize bert embeddings, False if not")
argparser.add_argument(
"-normalize",
"--normalize",
action='store_true',
default=False,
help="True if add normalization across voxels, False if not")
argparser.add_argument("-permutation",
"--permutation",
action='store_true',
default=False,
help="True if permutation, False if not")
argparser.add_argument(
"-permutation_region",
"--permutation_region",
action='store_true',
default=False,
help="True if permutation by brain region, False if not")
### PLOTTING ###
argparser.add_argument(
"-which_layer",
"--which_layer",
help="Layer of interest in [1: total number of layers]",
type=int,
default=1)
### SPECIFY FOR SINGLE SUBJECT OR GROUP LEVEL ANALYSIS ###
argparser.add_argument("-single_subject",
"--single_subject",
help="if single subject analysis",
action='store_true',
default=False)
argparser.add_argument("-group_level",
"--group_level",
help="if group level analysis",
action='store_true',
default=False)
argparser.add_argument("-searchlight",
"--searchlight",
help="if searchlight",
action='store_true',
default=False)
### SPECIFY FOR ONE LAYER OR DIFFERENCE IN LAYERS ###
argparser.add_argument("-single_layer",
"--single_layer",
help="if single layer significance",
action='store_true',
default=False)
argparser.add_argument("-across_layer",
"--across_layer",
help="if across layer depth significance",
action='store_true',
default=False)
### SPECIFY WHICH METRIC ###
argparser.add_argument("-fdr",
"--fdr",
help="if apply FDR",
action='store_true',
default=False)
argparser.add_argument("-llh",
"--llh",
action='store_true',
default=False,
help="True if calculate likelihood, False if not")
argparser.add_argument("-ranking",
"--ranking",
action='store_true',
default=False,
help="True if calculate ranking, False if not")
argparser.add_argument("-rmse",
"--rmse",
action='store_true',
default=False,
help="True if calculate rmse, False if not")
argparser.add_argument("-rsa",
"--rsa",
action='store_true',
default=False,
help="True if calculate rsa, False if not")
### UPDATE FILE PATHS HERE ###
argparser.add_argument(
"--fmri_path",
default="/n/shieber_lab/Lab/users/cjou/fmri/",
type=str,
help="file path to fMRI data on the Odyssey cluster")
argparser.add_argument(
"--to_save_path",
default="/n/shieber_lab/Lab/users/cjou/",
type=str,
help="file path to and create rmse/ranking/llh on the Odyssey cluster")
### UPDATE FILE PATHS HERE ###
args = argparser.parse_args()
if args.num_layers != 12 and args.bert:
print("error: please ensure bert has 12 layers")
exit()
if args.num_layers != 1 and (args.word2vec or args.random
or args.permutation or args.glove):
print("error: please ensure baseline has 1 layerc")
exit()
if not args.fdr and not args.llh and not args.ranking and not args.rmse:
print("error: select at least 1 metric of correlation")
exit()
print("NUMBER OF LAYERS: " + str(args.num_layers))
subjects = [1, 2, 4, 5, 7, 8, 9, 10, 11]
direction, validate, rlabel, elabel, glabel, w2vlabel, bertlabel, plabel, prlabel = helper.generate_labels(
args)
# print("generating file names...")
# layer1_file_name = generate_file_name(args, args.layer1)
# layer2_file_name = generate_file_name(args, args.layer2)
# print("retrieving file contents...")
# layer1 = get_file(args, layer1_file_name)
# layer2 = get_file(args, layer2_file_name)
# print("evaluating layers...")
# diff = compare_layers(layer1, layer2)
# print("DIFF")
# print(np.sum(diff))
if args.fdr:
metric = "fdr"
if args.rmse:
metric = "rmse"
if args.rsa:
metric = "rsa"
if args.ranking:
metric = "ranking"
if args.llh:
metric = "llh"
# generate heatmap
if args.single_subject and args.across_layer:
heatmap_differences = np.zeros((args.num_layers, args.num_layers))
for l1 in list(range(1, args.num_layers + 1)):
for l2 in list(range(l1, args.num_layers + 1)):
print("generating file names...")
layer1_file_name = generate_file_name(args,
args.subject_number, l1)
layer2_file_name = generate_file_name(args,
args.subject_number, l2)
print("retrieving file contents...")
layer1, pvals = get_file(args, layer1_file_name)
layer2, pvals = get_file(args, layer2_file_name)
diff = compare_layers(layer1, layer2)
pvals = stats.ttest_rel(layer1, layer2)
heatmap_differences[l1 - 1][l2 - 1] = np.sum(np.abs(diff))
heatmap_differences[l2 - 1][l1 - 1] = np.sum(np.abs(diff))
print(heatmap_differences.shape)
print(heatmap_differences)
Index = ['layer' + str(i) for i in range(1, args.num_layers + 1)]
df = pd.DataFrame(heatmap_differences, index=Index, columns=Index)
# plt.pcolor(df)
# plt.yticks(np.arange(0.5, len(df.index), 1), df.index)
# plt.xticks(np.arange(0.5, len(df.columns), 1), df.columns)
# plt.show()
sns.heatmap(df)
if args.bert:
file_name = "bert{}{}subj{}_{}_heatmap".format(
direction,
validate,
args.subject_number,
args.agg_type,
)
else:
specific_file = str(plabel) + str(prlabel) + str(rlabel) + str(
elabel
) + str(glabel) + str(w2vlabel) + str(bertlabel) + str(
direction
) + str(
validate
) + "-subj{}-parallel-english-to-{}-model-{}layer-{}-pred-layer-{}"
file_name = specific_file.format(args.subject_number,
args.language, args.num_layers,
"brnn", args.agg_type)
plt.savefig("../" + str(file_name) + str(metric) + ".png",
bbox_inches='tight')
# plt.show()
# pval = calculate_pval(layer1, layer2)
# print("pval")
# print(pval)
# save_file(args, diff, "difference_" + a)
# save_file(args, pval, "pval_" + )
if args.group_level and args.across_layer:
common_space = find_common_brain_space(args, subjects,
args.which_layer)
a, b, c = common_space.shape
heatmap_differences = np.zeros((args.num_layers, args.num_layers))
for l1 in list(range(1, args.num_layers + 1)):
for l2 in list(range(l1, args.num_layers + 1)):
# values_to_plot = np.zeros((len(subjects),a,b,c))
layer1_vals = np.zeros((len(subjects), a, b, c))
layer2_vals = np.zeros((len(subjects), a, b, c))
group_avgs = []
group_pvals = []
for subj_index in range(len(subjects)):
layer1_file_name = generate_file_name(args, subj_index, l1)
layer2_file_name = generate_file_name(args, subj_index, l2)
layer1, pvals = get_file(args, layer1_file_name)
layer2, pvals = get_file(args, layer2_file_name)
common_per_layer1 = layer1[common_space.astype(bool)]
common_per_layer2 = layer2[common_space.astype(bool)]
# pvals_per_layer = pvals[common_space.astype(bool)]
# values_to_plot[subj_index] = common_per_layer
# pvalues[subj_index] = pvals_per_layer
layer1_vals[subj_index] = common_per_layer1
layer2_vals[subj_index] = common_per_layer2
diff = compare_layers(common_per_layer1, common_per_layer2)
group_avgs.append(diff)
pvals = stats.ttest_rel(layer1_vals, layer2_vals)
heatmap_differences[l1 - 1][l2 - 1] = np.sum(
np.abs(np.mean(avgs, axis=0)))
heatmap_differences[l2 - 1][l1 - 1] = np.sum(
np.abs(np.mean(avgs, axis=0)))
print(heatmap_differences.shape)
print(heatmap_differences)
Index = ['layer' + str(i) for i in range(1, args.num_layers + 1)]
df = pd.DataFrame(heatmap_differences, index=Index, columns=Index)
# plt.pcolor(df)
# plt.yticks(np.arange(0.5, len(df.index), 1), df.index)
# plt.xticks(np.arange(0.5, len(df.columns), 1), df.columns)
# plt.show()
sns.heatmap(df)
plt.show()
pass
if args.group_level and args.single_layer:
common_space = find_common_brain_space(args, subjects,
args.which_layer)
a, b, c = common_space.shape
corr = np.zeros((len(subjects), a, b, c))
pvalues = np.zeros((len(subjects), a, b, c))
# get common values
for subj_index in range(len(subjects)):
layer_file_name = generate_file_name(args, subjects[subj_index],
args.which_layer)
layer, pvals = get_file(args, args.which_layer)
common_per_layer = layer[common_space.astype(bool)]
pvals_per_layer = pvals[common_space.astype(bool)]
corr[subj_index] = common_per_layer
pvalues[subj_index] = pvals_per_layer
group_pvals = np.apply_along_axis(calculate_ttest, 0, pvalues)
group_corrs = np.mean(corr, axis=0)
save_location = str(
args.to_save_path) + "fdr/group_level_single_layer_" + str(
args.which_layer)
volmask = pickle.load(
open(
str(args.to_save_path) + "subj" + str(args.subject_number) +
"/volmask.p", "rb"))
_ = helper.transform_coordinates(group_corrs,
volmask,
save_location,
"fdr",
pvals=group_pvals)
print("done.")
return
