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bootstrap_create_figures.py
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bootstrap_create_figures.py
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#!/usr/bin/env python3
"""
Script to create Figure 2 of the paper.
"""
from pathlib import Path
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
from utils import load_dataset
PROJECT_ROOT = Path.cwd()
def main():
"""Create elements for figure 2 of the paper"""
# ----------------------------------------------------------------------------
n_bootstrap = 1000
model_name = 'supervised_aae'
outputs_dir = PROJECT_ROOT / 'outputs'
bootstrap_dir = outputs_dir / 'bootstrap_analysis'
model_dir = bootstrap_dir / model_name
# ----------------------------------------------------------------------------
dataset_name = 'ADNI'
participants_path = PROJECT_ROOT / 'data' / dataset_name / 'participants.tsv'
freesurfer_path = PROJECT_ROOT / 'data' / dataset_name / 'freesurferData.csv'
ids_path = PROJECT_ROOT / 'outputs' / (dataset_name + '_homogeneous_ids.csv')
adni_df = load_dataset(participants_path, ids_path, freesurfer_path)
mean_adni_list = []
for i_bootstrap in tqdm(range(n_bootstrap)):
bootstrap_model_dir = model_dir / '{:03d}'.format(i_bootstrap)
output_dataset_dir = bootstrap_model_dir / dataset_name
output_dataset_dir.mkdir(exist_ok=True)
reconstruction_error_df = pd.read_csv(output_dataset_dir / 'reconstruction_error.csv')
error_hc = reconstruction_error_df.loc[adni_df['Diagn'] == 1]['Reconstruction error']
error_emci = reconstruction_error_df.loc[adni_df['Diagn'] == 27]['Reconstruction error']
error_lmci = reconstruction_error_df.loc[adni_df['Diagn'] == 28]['Reconstruction error']
error_ad = reconstruction_error_df.loc[adni_df['Diagn'] == 17]['Reconstruction error']
mean_adni_list.append([error_hc.mean(), error_emci.mean(), error_lmci.mean(), error_ad.mean()])
mean_adni_list = np.array(mean_adni_list)
plt.hlines(range(4),
np.percentile(mean_adni_list, 2.5, axis=0),
np.percentile(mean_adni_list, 97.5, axis=0))
plt.plot(np.mean(mean_adni_list, axis=0), range(4), 's', color='k')
plt.savefig(bootstrap_dir / 'ADNI.eps', format='eps')
plt.close()
plt.clf()
results = pd.DataFrame(columns={'Measure', 'HC', 'EMCI', 'LMCI', 'AD'})
results = results.append({'Measure': 'Mean',
'HC': np.mean(mean_adni_list, axis=0)[0],
'EMCI': np.mean(mean_adni_list, axis=0)[1],
'LMCI': np.mean(mean_adni_list, axis=0)[2],
'AD': np.mean(mean_adni_list, axis=0)[3], }, ignore_index=True)
results = results.append({'Measure': 'Lower',
'HC': np.percentile(mean_adni_list, 2.5, axis=0)[0],
'EMCI': np.percentile(mean_adni_list, 2.5, axis=0)[1],
'LMCI': np.percentile(mean_adni_list, 2.5, axis=0)[2],
'AD': np.percentile(mean_adni_list, 2.5, axis=0)[3], }, ignore_index=True)
results = results.append({'Measure': 'Upper',
'HC': np.percentile(mean_adni_list, 97.5, axis=0)[0],
'EMCI': np.percentile(mean_adni_list, 97.5, axis=0)[1],
'LMCI': np.percentile(mean_adni_list, 97.5, axis=0)[2],
'AD': np.percentile(mean_adni_list, 97.5, axis=0)[3], }, ignore_index=True)
results.to_csv(bootstrap_dir / dataset_name / 'deviations.csv', index=False)
# ----------------------------------------------------------------------------
dataset_name = 'AIBL'
participants_path = PROJECT_ROOT / 'data' / dataset_name / 'participants.tsv'
freesurfer_path = PROJECT_ROOT / 'data' / dataset_name / 'freesurferData.csv'
ids_path = PROJECT_ROOT / 'outputs' / (dataset_name + '_homogeneous_ids.csv')
brescia_df = load_dataset(participants_path, ids_path, freesurfer_path)
mean_brescia_list = []
for i_bootstrap in tqdm(range(n_bootstrap)):
bootstrap_model_dir = model_dir / '{:03d}'.format(i_bootstrap)
output_dataset_dir = bootstrap_model_dir / dataset_name
output_dataset_dir.mkdir(exist_ok=True)
reconstruction_error_df = pd.read_csv(output_dataset_dir / 'reconstruction_error.csv')
error_hc = reconstruction_error_df.loc[brescia_df['Diagn'] == 1]['Reconstruction error']
error_mci = reconstruction_error_df.loc[brescia_df['Diagn'] == 18]['Reconstruction error']
error_ad = reconstruction_error_df.loc[brescia_df['Diagn'] == 17]['Reconstruction error']
mean_brescia_list.append([error_hc.mean(), error_mci.mean(), error_ad.mean()])
mean_brescia_list = np.array(mean_brescia_list)
plt.hlines(range(3),
np.percentile(mean_brescia_list, 2.5, axis=0),
np.percentile(mean_brescia_list, 97.5, axis=0))
plt.plot(np.mean(mean_brescia_list, axis=0), range(3), 's', color='k')
plt.savefig(bootstrap_dir / 'AIBL.eps', format='eps')
plt.close()
plt.clf()
results = pd.DataFrame(columns={'Measure', 'HC', 'MCI', 'AD'})
results = results.append({'Measure': 'Mean',
'HC': np.mean(mean_brescia_list, axis=0)[0],
'MCI': np.mean(mean_brescia_list, axis=0)[1],
'AD': np.mean(mean_brescia_list, axis=0)[2], }, ignore_index=True)
results = results.append({'Measure': 'Lower',
'HC': np.percentile(mean_brescia_list, 2.5, axis=0)[0],
'MCI': np.percentile(mean_brescia_list, 2.5, axis=0)[1],
'AD': np.percentile(mean_brescia_list, 2.5, axis=0)[2], }, ignore_index=True)
results = results.append({'Measure': 'Upper',
'HC': np.percentile(mean_brescia_list, 97.5, axis=0)[0],
'MCI': np.percentile(mean_brescia_list, 97.5, axis=0)[1],
'AD': np.percentile(mean_brescia_list, 97.5, axis=0)[2], }, ignore_index=True)
results.to_csv(bootstrap_dir / dataset_name / 'deviations.csv', index=False)
# ----------------------------------------------------------------------------
dataset_name = 'TOMC'
participants_path = PROJECT_ROOT / 'data' / dataset_name / 'participants.tsv'
freesurfer_path = PROJECT_ROOT / 'data' / dataset_name / 'freesurferData.csv'
ids_path = PROJECT_ROOT / 'outputs' / (dataset_name + '_homogeneous_ids.csv')
brescia_df = load_dataset(participants_path, ids_path, freesurfer_path)
mean_brescia_list = []
for i_bootstrap in tqdm(range(n_bootstrap)):
bootstrap_model_dir = model_dir / '{:03d}'.format(i_bootstrap)
output_dataset_dir = bootstrap_model_dir / dataset_name
output_dataset_dir.mkdir(exist_ok=True)
reconstruction_error_df = pd.read_csv(output_dataset_dir / 'reconstruction_error.csv')
error_hc = reconstruction_error_df.loc[brescia_df['Diagn'] == 1]['Reconstruction error']
error_mci = reconstruction_error_df.loc[brescia_df['Diagn'] == 18]['Reconstruction error']
error_ad = reconstruction_error_df.loc[brescia_df['Diagn'] == 17]['Reconstruction error']
mean_brescia_list.append([error_hc.mean(), error_mci.mean(), error_ad.mean()])
mean_brescia_list = np.array(mean_brescia_list)
plt.hlines(range(3),
np.percentile(mean_brescia_list, 2.5, axis=0),
np.percentile(mean_brescia_list, 97.5, axis=0))
plt.plot(np.mean(mean_brescia_list, axis=0), range(3), 's', color='k')
plt.savefig(bootstrap_dir / 'TOMC.eps', format='eps')
plt.close()
plt.clf()
results = pd.DataFrame(columns={'Measure', 'HC', 'MCI', 'AD'})
results = results.append({'Measure': 'Mean',
'HC': np.mean(mean_brescia_list, axis=0)[0],
'MCI': np.mean(mean_brescia_list, axis=0)[1],
'AD': np.mean(mean_brescia_list, axis=0)[2], }, ignore_index=True)
results = results.append({'Measure': 'Lower',
'HC': np.percentile(mean_brescia_list, 2.5, axis=0)[0],
'MCI': np.percentile(mean_brescia_list, 2.5, axis=0)[1],
'AD': np.percentile(mean_brescia_list, 2.5, axis=0)[2], }, ignore_index=True)
results = results.append({'Measure': 'Upper',
'HC': np.percentile(mean_brescia_list, 97.5, axis=0)[0],
'MCI': np.percentile(mean_brescia_list, 97.5, axis=0)[1],
'AD': np.percentile(mean_brescia_list, 97.5, axis=0)[2], }, ignore_index=True)
results.to_csv(bootstrap_dir / dataset_name / 'deviations.csv', index=False)
# ----------------------------------------------------------------------------
dataset_name = 'OASIS1'
participants_path = PROJECT_ROOT / 'data' / dataset_name / 'participants.tsv'
freesurfer_path = PROJECT_ROOT / 'data' / dataset_name / 'freesurferData.csv'
ids_path = PROJECT_ROOT / 'outputs' / (dataset_name + '_homogeneous_ids.csv')
oasis1_df = load_dataset(participants_path, ids_path, freesurfer_path)
mean_oasis1_list = []
for i_bootstrap in tqdm(range(n_bootstrap)):
bootstrap_model_dir = model_dir / '{:03d}'.format(i_bootstrap)
output_dataset_dir = bootstrap_model_dir / dataset_name
output_dataset_dir.mkdir(exist_ok=True)
reconstruction_error_df = pd.read_csv(output_dataset_dir / 'reconstruction_error.csv')
error_hc = reconstruction_error_df.loc[oasis1_df['Diagn'] == 1]['Reconstruction error']
error_ad = reconstruction_error_df.loc[oasis1_df['Diagn'] == 17]['Reconstruction error']
mean_oasis1_list.append([error_hc.mean(), error_ad.mean()])
mean_oasis1_list = np.array(mean_oasis1_list)
plt.hlines(range(2),
np.percentile(mean_oasis1_list, 2.5, axis=0),
np.percentile(mean_oasis1_list, 97.5, axis=0))
plt.plot(np.mean(mean_oasis1_list, axis=0), range(2), 's', color='k')
plt.savefig(bootstrap_dir / 'OASIS1.eps', format='eps')
plt.close()
plt.clf()
results = pd.DataFrame(columns={'Measure', 'HC', 'AD'})
results = results.append({'Measure': 'Mean',
'HC': np.mean(mean_oasis1_list, axis=0)[0],
'AD': np.mean(mean_oasis1_list, axis=0)[1], }, ignore_index=True)
results = results.append({'Measure': 'Lower',
'HC': np.percentile(mean_oasis1_list, 2.5, axis=0)[0],
'AD': np.percentile(mean_oasis1_list, 2.5, axis=0)[1], }, ignore_index=True)
results = results.append({'Measure': 'Upper',
'HC': np.percentile(mean_oasis1_list, 97.5, axis=0)[0],
'AD': np.percentile(mean_oasis1_list, 97.5, axis=0)[1], }, ignore_index=True)
results.to_csv(bootstrap_dir / dataset_name / 'deviations.csv', index=False)
# ----------------------------------------------------------------------------
dataset_name = 'MIRIAD'
participants_path = PROJECT_ROOT / 'data' / dataset_name / 'participants.tsv'
freesurfer_path = PROJECT_ROOT / 'data' / dataset_name / 'freesurferData.csv'
ids_path = PROJECT_ROOT / 'outputs' / (dataset_name + '_homogeneous_ids.csv')
oasis1_df = load_dataset(participants_path, ids_path, freesurfer_path)
mean_oasis1_list = []
for i_bootstrap in tqdm(range(n_bootstrap)):
bootstrap_model_dir = model_dir / '{:03d}'.format(i_bootstrap)
output_dataset_dir = bootstrap_model_dir / dataset_name
output_dataset_dir.mkdir(exist_ok=True)
reconstruction_error_df = pd.read_csv(output_dataset_dir / 'reconstruction_error.csv')
error_hc = reconstruction_error_df.loc[oasis1_df['Diagn'] == 1]['Reconstruction error']
error_ad = reconstruction_error_df.loc[oasis1_df['Diagn'] == 17]['Reconstruction error']
mean_oasis1_list.append([error_hc.mean(), error_ad.mean()])
mean_oasis1_list = np.array(mean_oasis1_list)
plt.hlines(range(2),
np.percentile(mean_oasis1_list, 2.5, axis=0),
np.percentile(mean_oasis1_list, 97.5, axis=0))
plt.plot(np.mean(mean_oasis1_list, axis=0), range(2), 's', color='k')
plt.savefig(bootstrap_dir / 'MIRIAD.eps', format='eps')
plt.close()
plt.clf()
results = pd.DataFrame(columns={'Measure', 'HC', 'AD'})
results = results.append({'Measure': 'Mean',
'HC': np.mean(mean_oasis1_list, axis=0)[0],
'AD': np.mean(mean_oasis1_list, axis=0)[1], }, ignore_index=True)
results = results.append({'Measure': 'Lower',
'HC': np.percentile(mean_oasis1_list, 2.5, axis=0)[0],
'AD': np.percentile(mean_oasis1_list, 2.5, axis=0)[1], }, ignore_index=True)
results = results.append({'Measure': 'Upper',
'HC': np.percentile(mean_oasis1_list, 97.5, axis=0)[0],
'AD': np.percentile(mean_oasis1_list, 97.5, axis=0)[1], }, ignore_index=True)
results.to_csv(bootstrap_dir / dataset_name / 'deviations.csv', index=False)
if __name__ == "__main__":
main()