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fmri_power_analysis_comparison.py
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import argparse
import matplotlib.pyplot as plt
import numpy as np
import os
import seaborn as sns
import shutil
from brainpedia.brainpedia import Brainpedia
from brainpedia.fmri_processing import invert_preprocessor_scaling
from utils.multiple_comparison import bootstrap_rejecting_voxels_mask, fmri_power_calculations
from nilearn import plotting
from utils.sampling import *
# Parse arguments
parser = argparse.ArgumentParser(
description="Compare classical two sample t test to non-parametric tests for real and synthetic fMRI brain imaging datasets.")
parser.add_argument('real_dataset_dir',
help='the directory containing the first real fMRI dataset')
parser.add_argument('real_dataset_cache_dir',
help='the directory to use as a cache for real dataset 1 preprocessing')
parser.add_argument(
'syn_dataset_dir', help='the directory containing the synthetic fMRI dataset generated from a model trained on real dataset 1')
parser.add_argument('syn_dataset_cache_dir',
help='the directory to use as a cache for synthetic dataset 1 preprocessing')
parser.add_argument(
'tag', help='the dataset cognitive process tag describing what tag should split the data')
parser.add_argument(
'output_dir', help='the directory to save power analysis results')
args = parser.parse_args()
OUTPUT_DATA_DIR = args.output_dir + 'data/'
# Setup output directory
# shutil.rmtree(args.output_dir, ignore_errors=True)
try:
os.makedirs(args.output_dir)
except:
pass
try:
os.makedirs(OUTPUT_DATA_DIR)
except:
pass
# Load datasets
DOWNSAMPLE_SCALE = 0.25
MULTI_TAG_LABEL_ENCODING = False
real_dataset_brainpedia = Brainpedia(data_dirs=[args.real_dataset_dir],
cache_dir=args.real_dataset_cache_dir,
scale=DOWNSAMPLE_SCALE,
multi_tag_label_encoding=MULTI_TAG_LABEL_ENCODING)
syn_dataset_brainpedia = Brainpedia(data_dirs=[args.syn_dataset_dir],
cache_dir=args.syn_dataset_cache_dir,
scale=DOWNSAMPLE_SCALE,
multi_tag_label_encoding=MULTI_TAG_LABEL_ENCODING)
real_dataset, real_dataset_tags = real_dataset_brainpedia.all_data()
syn_dataset, syn_dataset_tags = syn_dataset_brainpedia.all_data()
# Filter datasets into data with tag and data without
real_dataset_1 = []
syn_dataset_1 = []
real_dataset_2 = []
syn_dataset_2 = []
for real_data, syn_data, real_tags, syn_tags in zip(real_dataset, syn_dataset, real_dataset_tags, syn_dataset_tags):
if args.tag in real_dataset_brainpedia.preprocessor.decode_label(real_tags):
real_dataset_1.append(real_data)
else:
real_dataset_2.append(real_data)
if args.tag in syn_dataset_brainpedia.preprocessor.decode_label(syn_tags):
syn_dataset_1.append(syn_data)
else:
syn_dataset_2.append(syn_data)
# Trim real datasets to the same length
real_dataset_length = min(len(real_dataset_1), len(real_dataset_2))
real_dataset_1 = np.array(real_dataset_1[:real_dataset_length])
real_dataset_2 = np.array(real_dataset_2[:real_dataset_length])
# Trim synthetic datasets to the same length
syn_dataset_length = min(len(syn_dataset_1), len(syn_dataset_2))
syn_dataset_1 = np.array(syn_dataset_1[:syn_dataset_length])
syn_dataset_2 = np.array(syn_dataset_2[:syn_dataset_length])
# Plot examples from datasets
real_dataset_1_img = invert_preprocessor_scaling(
real_dataset_1[0].squeeze(), real_dataset_brainpedia.preprocessor)
real_dataset_2_img = invert_preprocessor_scaling(
real_dataset_2[0].squeeze(), real_dataset_brainpedia.preprocessor)
syn_dataset_1_img = invert_preprocessor_scaling(
syn_dataset_1[0].squeeze(), syn_dataset_brainpedia.preprocessor)
syn_dataset_2_img = invert_preprocessor_scaling(
syn_dataset_2[2].squeeze(), syn_dataset_brainpedia.preprocessor)
figure, axes = plt.subplots(nrows=6, ncols=1, figsize=(15, 40))
plotting.plot_glass_brain(real_dataset_1_img, threshold='auto',
title="[REAL {0}]".format(args.tag), axes=axes[0])
plotting.plot_glass_brain(syn_dataset_1_img, threshold='auto',
title="[SYN {0}]".format(args.tag), axes=axes[1])
plotting.plot_glass_brain(real_dataset_2_img, threshold='auto',
title="[REAL {0}]".format('NON-' + args.tag), axes=axes[2])
plotting.plot_glass_brain(syn_dataset_2_img, threshold='auto',
title="[SYN {0}]".format('NON-' + args.tag), axes=axes[3])
# Compute statistical significance weights of each voxel in non-visual vs
# visual
num_trials = 5
k = 10
real_rejecting_voxels_mask = bootstrap_rejecting_voxels_mask(
real_dataset_1.squeeze(), real_dataset_2.squeeze(), k=k)
# Compute power for various n
n = np.linspace(10, 100, num=25)
fdr_test_p_values_for_n = np.zeros((len(n), num_trials, k))
syn_fdr_test_p_values_for_n = np.zeros((len(n), num_trials, k))
mmd_test_p_values_for_n = np.zeros((len(n), num_trials, k))
syn_mmd_test_p_values_for_n = np.zeros((len(n), num_trials, k))
fdr_test_power_for_n = np.zeros((len(n), num_trials))
syn_fdr_test_power_for_n = np.zeros((len(n), num_trials))
mmd_test_power_for_n = np.zeros((len(n), num_trials))
syn_mmd_test_power_for_n = np.zeros((len(n), num_trials))
percent_rejecting_voxels_syn_for_n = np.zeros((len(n), k))
percent_rejecting_voxels_real_for_n = np.zeros((len(n), k))
wtp_syn_for_n = np.zeros((len(n), k))
wtn_syn_for_n = np.zeros((len(n), k))
wfp_syn_for_n = np.zeros((len(n), k))
wfn_syn_for_n = np.zeros((len(n), k))
wtp_real_for_n = np.zeros((len(n), k))
wtn_real_for_n = np.zeros((len(n), k))
wfp_real_for_n = np.zeros((len(n), k))
wfn_real_for_n = np.zeros((len(n), k))
for i in range(len(n)):
# Determine sample sizes to draw from synthetic and real datasets
# Note: there is limited real data. When there is none left, simply use the
# max available amount of data.
syn_n = int(n[i])
real_n = min(real_dataset_1.shape[0], int(n[i]))
for t in range(num_trials):
fdr_real_p_val, mmd_p_val, fdr_real_power, mmd_power, percent_rejecting_voxels_real, wtp_real, wtn_real, wfp_real, wfn_real = fmri_power_calculations(
real_dataset_1, real_dataset_2, real_n, real_n, real_rejecting_voxels_mask, k=k)
fdr_syn_p_val, mmd_syn_p_val, fdr_syn_power, mmd_syn_power, percent_rejecting_voxels_syn, wtp_syn, wtn_syn, wfp_syn, wfn_syn = fmri_power_calculations(
syn_dataset_1, syn_dataset_2, syn_n, syn_n, real_rejecting_voxels_mask, k=k)
fdr_test_p_values_for_n[i][t][:] = fdr_real_p_val[:]
syn_fdr_test_p_values_for_n[i][t][:] = fdr_syn_p_val[:]
mmd_test_p_values_for_n[i][t][:] = mmd_p_val[:]
syn_mmd_test_p_values_for_n[i][t][:] = mmd_syn_p_val[:]
fdr_test_power_for_n[i][t] = fdr_real_power
syn_fdr_test_power_for_n[i][t] = fdr_syn_power
mmd_test_power_for_n[i][t] = mmd_power
syn_mmd_test_power_for_n[i][t] = mmd_syn_power
percent_rejecting_voxels_syn_for_n[i][:] = percent_rejecting_voxels_syn
percent_rejecting_voxels_real_for_n[i][:] = percent_rejecting_voxels_real
wtp_syn_for_n[i][:] = wtp_syn[:]
wtn_syn_for_n[i][:] = wtn_syn[:]
wfp_syn_for_n[i][:] = wfp_syn[:]
wfn_syn_for_n[i][:] = wfn_syn[:]
wtp_real_for_n[i][:] = wtp_real[:]
wtn_real_for_n[i][:] = wtn_real[:]
wfp_real_for_n[i][:] = wfp_real[:]
wfn_real_for_n[i][:] = wfn_real[:]
print("PERCENT COMPLETE: {0:.2f}%\r".format(
100 * float(i + 1) / float(len(n))), end='')
# Calculate Beta value for every trial and every sample size
def compute_beta(real_pvals, syn_pvals, alpha=0.05, k=50):
l = 0.0
h = 1.0
for _ in range(k):
beta = (l + h) / 2.0
syn_reject_too_often = False
for n in range(real_pvals.shape[0]):
for trial in range(real_pvals.shape[1]):
avg_real_rejection = np.mean(real_pvals[n][trial] < alpha)
avg_syn_rejection = np.mean(syn_pvals[n][trial] + beta < alpha)
if avg_syn_rejection > avg_real_rejection:
syn_reject_too_often = True
if syn_reject_too_often:
l = beta
else:
h = beta
return beta
fdr_beta = compute_beta(fdr_test_p_values_for_n, syn_fdr_test_p_values_for_n)
mmd_beta = compute_beta(mmd_test_p_values_for_n, syn_mmd_test_p_values_for_n)
#((len(n), num_trials, k))
conservative_syn_fdr_test_p_values_for_n = np.copy(
syn_fdr_test_p_values_for_n) + fdr_beta
conservative_syn_fdr_test_power = np.mean(
conservative_syn_fdr_test_p_values_for_n < 0.05, axis=2)
conservative_syn_mmd_test_p_values_for_n = np.copy(
syn_mmd_test_p_values_for_n) + mmd_beta
conservative_syn_mmd_test_power = np.mean(
conservative_syn_mmd_test_p_values_for_n < 0.05, axis=2)
# Plot curve of n vs FDR corrected t test power
sns.tsplot(data=fdr_test_power_for_n.T, time=n, ci=[
68, 95], color='blue', condition='REAL', ax=axes[4])
sns.tsplot(data=syn_fdr_test_power_for_n.T, time=n, ci=[
68, 95], color='orange', condition='SYN', ax=axes[4])
sns.tsplot(data=conservative_syn_fdr_test_power.T, time=n, ci=[
68, 95], color='green', condition='SYN CONSERVATIVE', ax=axes[4])
axes[4].set_title('Sample Size vs FDR Corrected T Test Power')
axes[4].set_xlabel('Sample Size, Beta = %f' % (fdr_beta))
axes[4].set_ylabel('Power')
axes[4].set_ylim([-0.1, 1.1])
axes[4].legend(loc="upper right")
np.save('{0}[fmri_power_analysis]_[{1}]_fdr_power_real.npy'.format(
OUTPUT_DATA_DIR, args.tag), fdr_test_power_for_n)
np.save('{0}[fmri_power_analysis]_[{1}]_fdr_power_syn.npy'.format(
OUTPUT_DATA_DIR, args.tag), syn_fdr_test_power_for_n)
np.save('{0}[fmri_power_analysis]_[{1}]_fdr_power_syncon.npy'.format(
OUTPUT_DATA_DIR, args.tag), conservative_syn_fdr_test_power)
# Plot curve of n vs MMD test power
sns.tsplot(data=mmd_test_power_for_n.T, time=n, ci=[
68, 95], color='blue', condition='REAL', ax=axes[5])
sns.tsplot(data=syn_mmd_test_power_for_n.T, time=n, ci=[
68, 95], color='orange', condition='SYN', ax=axes[5])
sns.tsplot(data=conservative_syn_mmd_test_power.T, time=n, ci=[
68, 95], color='green', condition='SYN CONSERVATIVE', ax=axes[5])
axes[5].set_title('Sample Size vs MMD Test Power')
axes[5].set_xlabel('Sample Size, Beta = %f' % (mmd_beta))
axes[5].set_ylabel('Power')
axes[5].set_ylim([-0.1, 1.1])
axes[5].legend(loc="upper right")
np.save('{0}[fmri_power_analysis]_[{1}]_mmd_power_real.npy'.format(
OUTPUT_DATA_DIR, args.tag), mmd_test_power_for_n)
np.save('{0}[fmri_power_analysis]_[{1}]_mmd_power_syn.npy'.format(
OUTPUT_DATA_DIR, args.tag), syn_mmd_test_power_for_n)
np.save('{0}[fmri_power_analysis]_[{1}]_mmd_power_syncon.npy'.format(
OUTPUT_DATA_DIR, args.tag), conservative_syn_mmd_test_power)
# # Plot curve of percent rejecting voxels
# sns.tsplot(data=percent_rejecting_voxels_real_for_n.T, time=n, ci=[
# 68, 95], color='blue', condition='REAL', ax=axes[6])
# sns.tsplot(data=percent_rejecting_voxels_syn_for_n.T, time=n, ci=[
# 68, 95], color='orange', condition='SYN', ax=axes[6])
# axes[6].set_title('Sample Size vs Percent Significant Voxels')
# axes[6].set_xlabel('Sample Size')
# axes[6].set_ylabel('% Sig Voxels')
# axes[6].legend(loc="upper right")
# # Plot curve of n vs rejection overlaps
# # True Positive
# sns.tsplot(data=wtp_real_for_n.T, time=n, ci=[
# 68, 95], color='blue', condition='REAL', ax=axes[7])
# sns.tsplot(data=wtp_syn_for_n.T, time=n, ci=[
# 68, 95], color='orange', condition='SYN', ax=axes[7])
# axes[7].set_title('Sample Size vs Weighted True Positive')
# axes[7].set_xlabel('Sample Size')
# axes[7].set_ylabel('W_TP')
# axes[7].legend(loc="upper right")
# # True Negative
# sns.tsplot(data=wtn_real_for_n.T, time=n, ci=[
# 68, 95], color='blue', condition='REAL', ax=axes[8])
# sns.tsplot(data=wtn_syn_for_n.T, time=n, ci=[
# 68, 95], color='orange', condition='SYN', ax=axes[8])
# axes[8].set_title('Sample Size vs Weighted True Negatives')
# axes[8].set_xlabel('Sample Size')
# axes[8].set_ylabel('W_TN')
# axes[8].legend(loc="upper right")
# # False Positive
# sns.tsplot(data=wfp_real_for_n.T, time=n, ci=[
# 68, 95], color='blue', condition='REAL', ax=axes[9])
# sns.tsplot(data=wfp_syn_for_n.T, time=n, ci=[
# 68, 95], color='orange', condition='SYN', ax=axes[9])
# axes[9].set_title('Sample Size vs Weighted False Positives')
# axes[9].set_xlabel('Sample Size')
# axes[9].set_ylabel('W_FP')
# axes[9].legend(loc="upper right")
# # False Negative
# sns.tsplot(data=wfn_real_for_n.T, time=n, ci=[
# 68, 95], color='blue', condition='REAL', ax=axes[10])
# sns.tsplot(data=wfn_syn_for_n.T, time=n, ci=[
# 68, 95], color='orange', condition='SYN', ax=axes[10])
# axes[10].set_title('Sample Size vs Weighted False Negatives')
# axes[10].set_xlabel('Sample Size')
# axes[10].set_ylabel('W_FN')
# axes[10].legend(loc="upper right")
# Save results
figure.subplots_adjust(hspace=0.5)
figure.savefig('{0}[fmri_power_analysis]_[{1}].pdf'.format(
args.output_dir, args.tag), format='pdf')