def select_converters(args, rids, diagnoses, dpis, dprs):
''' Select data from subjects that convert within 2 years from MCI to AD. '''
data_handler = DataHandler.get_data_handler(method=args.method)
measurements = data_handler.get_measurements_as_dict(
visits=['bl', 'm12', 'm24'],
no_regression=True,
select_training_set=True,
select_complete=True)
# Select RIDSs of converters
rids_select = set()
for rid in measurements:
if 0.25 <= measurements[rid]['bl']['DX.scan'] <= 0.75 and measurements[
rid]['m24']['DX.scan'] == 1.0:
rids_select.add(rid)
selected_rids = []
selected_diagnoses = []
selected_dpis = []
selected_dprs = []
for i, rid in enumerate(rids):
if rid in rids_select:
selected_rids.append(rid)
selected_diagnoses.append(diagnoses[i])
selected_dpis.append(dpis[i])
selected_dprs.append(dprs[i])
print log.RESULT, 'Selected {0} converting subjects.'.format(
len(selected_rids))
return selected_rids, selected_diagnoses, selected_dpis, selected_dprs
def select_nonconverters(args, rids, diagnoses, dpis, dprs):
''' Select data from MCI subjects that do not convert. '''
data_handler = DataHandler.get_data_handler(method=args.method)
measurements = data_handler.get_measurements_as_dict(visits=['bl', 'm12', 'm24'],
no_regression=True,
select_test_set=True,
select_complete=True)
# Select RIDSs of non-converters
rids_select = set()
for rid in measurements:
if 0.25 <= measurements[rid]['bl']['DX.scan'] <= 0.75 and 0.25 <= measurements[rid]['m24']['DX.scan'] <= 0.75:
rids_select.add(rid)
selected_rids = []
selected_diagnoses = []
selected_dpis = []
selected_dprs = []
for i, rid in enumerate(rids):
if rid in rids_select:
selected_rids.append(rid)
selected_diagnoses.append(diagnoses[i])
selected_dpis.append(dpis[i])
selected_dprs.append(dprs[i])
print log.RESULT, 'Selected {0} non-converting subjects.'.format(len(selected_rids))
return selected_rids, selected_diagnoses, selected_dpis, selected_dprs
def main():
# Parse input arguments
parser = argparse.ArgumentParser()
parser.add_argument('--estimate_dpr', action='store_true', help='recompute the dpis estimations')
parser.add_argument('--samples_file', type=str, default='measurements_sample.csv',
help='recompute the dpis estimations')
args = parser.parse_args()
# Read the measurements as dict from the csv file
measurements, biomarkers = read_measurements_from_cvs(args.samples_file)
visits = measurements[0].keys()
# Get estimates
data_handler = DataHandler.get_data_handler(method='all',
biomarkers=biomarkers,
phase='joint')
# Setup model
model = MultiBiomarkerProgressionModel()
for biomarker in biomarkers:
model_file = data_handler.get_model_file(biomarker)
model.add_model(biomarker, model_file)
fitter = ModelFitter(model)
# Estimate dpis (and dprs) and save data
if args.estimate_dpr:
rids, diagnoses, dpis, dprs = estimate_dpis_dprs(measurements, visits, fitter, phase='joint')
else:
rids, diagnoses, dpis = estimate_dpis(measurements, visits, fitter, phase='joint')
dprs = np.ones(len(dpis)).tolist()
# Plot the models with the fitted samples
for biomarker in biomarkers:
plot_biomarker(data_handler, biomarker, measurements, dpis[0], dprs[0])
def classify_converters(args, dpis_conv, dprs_conv, dpis_nonconv, dprs_nonconv):
print log.INFO, 'Analysing classification accuracies...'
dpis = np.concatenate((dpis_conv, dpis_nonconv))
dprs = np.concatenate((dprs_conv, dprs_nonconv))
labels = np.concatenate((np.ones(len(dpis_conv)), np.zeros(len(dpis_nonconv))))
# Assemble features
features = np.zeros((len(dpis), 2))
features[:, 0] = dpis
if args.estimate_dprs:
features[:, 1] = dprs
else:
# Copy DPIs as second features as LDA needs two features
features[:, 1] = dpis
features = preprocessing.scale(features)
acc, sens, spec = run_classification(args, features, labels)
print log.RESULT, '{0}-fold cross validation, converters vs. non-converters ACC={1:.2f}, SENS={2:.2f}, SPEC={3:.2f}'.format(args.num_folds, acc, sens, spec)
if args.latex_file is not None:
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
filename = os.path.join(data_handler.get_eval_folder(), args.latex_file)
print log.INFO, 'Writing classification results to {0}...'.format(filename)
with open(filename, 'a') as latex_file:
latex_file.write('{0} & {1} & {2:.2f} & {3:.2f} & {4:.2f}\\\\\n'.format(
args.method,
len(args.visits),
acc, sens, spec))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m', '--method', choices=DataHandler.get_method_choices(), default='all', help='the method to collect data for')
parser.add_argument('-b', '--biomarkers', nargs='+', default=None, help='name of the biomarker to be plotted')
parser.add_argument('-p', '--phase', default='mciad', choices=DataHandler.get_phase_choices(), help='the phase for which the model is to be trained')
parser.add_argument('-e', '--extrapolator', type=str, choices=['lin', 'sqrt', 'exp'], default='exp', help='the type of extrapolator')
parser.add_argument('--xlim', type=float, nargs=2, default=None, help='force certain x limits for plotting')
parser.add_argument('--ylim', type=float, nargs=2, default=None, help='force certain y limits for plotting')
parser.add_argument('--no_model', action='store_true', default=False, help='do not plot the fitted model')
parser.add_argument('--no_points', action='store_true', default=False, help='do not plot points')
parser.add_argument('--points_alpha', type=float, default=0.25, help='alpha value of the plotted points')
parser.add_argument('--no_densities', action='store_true', default=False, help='do not plot densities')
parser.add_argument('--no_sample_lines', action='store_true', default=False, help='do not plot the sample lines')
parser.add_argument('--only_densities', action='store_true', default=False, help='only plot densities')
parser.add_argument('--no_extrapolation', action='store_true', default=False, help='do not extrapolate the model')
parser.add_argument('--plot_eta', type=str, choices=['lambda', 'mu', 'sigma'], default=None, help='plot a predictor function')
parser.add_argument('--plot_errors', action='store_true', default=False, help='plot the errors')
parser.add_argument('--plot_synth_model', action='store_true', default=False, help='plot density distributions for synthetic data')
parser.add_argument('--plot_quantile_label', action='store_true', default=False, help='plot labels on the quantile curces')
parser.add_argument('--plot_donohue', action='store_true', default=False, help='plot the trajectory estimated with Donohue et al.')
parser.add_argument('--save_plots', action='store_true', default=False, help='save the plots with a default filename')
parser.add_argument('--plot_file', type=str, default=None, help='filename of the output file')
args = parser.parse_args()
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
for biomarker in data_handler.get_biomarker_names():
plot_model(args, data_handler, biomarker)
def classify_converters(args, dpis_conv, dprs_conv, dpis_nonconv,
dprs_nonconv):
print log.INFO, 'Analysing classification accuracies...'
dpis = np.concatenate((dpis_conv, dpis_nonconv))
dprs = np.concatenate((dprs_conv, dprs_nonconv))
labels = np.concatenate(
(np.ones(len(dpis_conv)), np.zeros(len(dpis_nonconv))))
# Assemble features
features = np.zeros((len(dpis), 2))
features[:, 0] = dpis
if args.estimate_dprs:
features[:, 1] = dprs
else:
# Copy DPIs as second features as LDA needs two features
features[:, 1] = dpis
features = preprocessing.scale(features)
acc, sens, spec = run_classification(args, features, labels)
print log.RESULT, '{0}-fold cross validation, converters vs. non-converters ACC={1:.2f}, SENS={2:.2f}, SPEC={3:.2f}'.format(
args.num_folds, acc, sens, spec)
if args.latex_file is not None:
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
filename = os.path.join(data_handler.get_eval_folder(),
args.latex_file)
print log.INFO, 'Writing classification results to {0}...'.format(
filename)
with open(filename, 'a') as latex_file:
latex_file.write(
'{0} & {1} & {2:.2f} & {3:.2f} & {4:.2f}\\\\\n'.format(
args.method, len(args.visits), acc, sens, spec))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m',
'--method',
choices=DataHandler.get_method_choices(),
default='all',
help='the method to collect data for')
parser.add_argument('-b',
'--biomarkers',
nargs='+',
default=None,
help='name of the biomarker to be plotted')
parser.add_argument('-p',
'--phase',
default='mciad',
choices=DataHandler.get_phase_choices(),
help='the phase for which the model is to be trained')
parser.add_argument('-e',
'--extrapolator',
type=str,
choices=['lin', 'sqrt', 'exp'],
default='exp',
help='the type of extrapolator')
args = parser.parse_args()
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
biomarkers = data_handler.get_biomarker_names()
if args.method == 'joint':
offsets = np.linspace(500, 3000, 26)
else:
offsets = np.linspace(-1000, 1000, 21)
all_diffs = np.zeros((len(offsets), len(biomarkers)))
for i, biomarker in enumerate(biomarkers):
diffs = get_model_differences(args, data_handler, biomarker, offsets)
all_diffs[:, i] = diffs
print biomarker, offsets[np.argmin(diffs)]
optimum_index = np.argmin(np.mean(all_diffs, axis=1))
print 'all', offsets[optimum_index]
mins = all_diffs[optimum_index, :] # np.min(all_diffs, axis=0)
indices = np.argsort(mins)
for i in indices:
print biomarkers[i], mins[i]
fig = plt.figure()
ax1 = plt.subplot(1, 1, 1)
ax1.plot(offsets, all_diffs, color='r')
ax1.plot(offsets, np.mean(all_diffs, axis=1), color='b')
plt.show()
plt.close(fig)
def print_to_latex(args, results_naive, results_model, num_subjects):
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
filename = os.path.join(data_handler.get_eval_folder(), args.latex_file)
with open(filename, 'a') as latex_file:
latex_file.write(
'{0} & {1} {2} & ${3:.2f}\pm{4:.2f}$ & ${5:.2f}$ & ${6:.2f}\pm{7:.2f}$ & ${8:.2f}$ & {9}\\\\\n'
.format(args.predict_biomarker, args.method, len(args.visits),
results_naive['MEAN'], results_naive['STD'],
results_naive['CORR'], results_model['MEAN'],
results_model['STD'], results_model['CORR'], num_subjects))
def main():
parser = argparse.ArgumentParser(description='Estimate model curves for biomarkers using VGAM.')
parser.add_argument('-m', '--method', choices=DataHandler.get_method_choices(), default='all', help='the method to collect data for')
parser.add_argument('-b', '--biomarkers', nargs='+', default=None, help='name of the biomarker to be plotted')
parser.add_argument('-e', '--extrapolator', type=str, choices=['lin', 'sqrt', 'exp'], default='exp', help='the type of extrapolator')
parser.add_argument('--plot_threshold', type=float, default=0.3, help='the threshold above which praphs are plotted')
parser.add_argument('--recompute_errors', action='store_true', help='recompute the matrix containing the fitting errors')
parser.add_argument('--search_range', nargs=3, default=(1000, 5000, 10), help='the range in which the offset is sought')
args = parser.parse_args()
# Get the data files and biomarkers
data_handler_joint = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase='joint')
biomarkers, offsets, errors, descriminativeness, overlap = get_fitting_data(args, data_handler_joint)
# Plot single biomarker fits
fig, ax = plt.subplots()
pt.setup_axes(plt, ax, xgrid=False)
ax.set_title('Optimal offset between CN/MCI and MCI/AD models')
ax.set_xlabel('Offset (days)')
ax.set_ylabel('Fitting error')
for i, biomarker in enumerate(biomarkers):
if descriminativeness[i] > args.plot_threshold:
print log.RESULT, 'Min error for {0} at {1}'.format(biomarker, offsets[np.argmin(errors[i, :])])
ax.plot(offsets, errors[i, :], label=biomarker, linestyle='--')
# Get optimal offset
mean_errors = np.mean(errors, 0)
weighted_mean_errors = np.dot(errors.T, descriminativeness) / np.sum(descriminativeness)
# Plot joint fit
ax.plot(offsets, mean_errors, label='Mean', linewidth=2, color='g')
ax.plot(offsets, weighted_mean_errors, label='Weighted mean', linewidth=2, color='r')
# Get and lot optimal offset
optimal_offset = offsets[np.argmin(mean_errors)]
optimal_offset_weighted = offsets[np.argmin(weighted_mean_errors)]
print log.RESULT, 'Optimal threshold: {0}'.format(optimal_offset)
print log.RESULT, 'Optimal threshold (weighted): {0}'.format(optimal_offset_weighted)
ax.axvline(optimal_offset, linestyle=':', color='g')
ax.axvline(optimal_offset_weighted, linestyle=':', color='r')
# Plot overlap
ax.axvline(overlap, color='0.15', linestyle=':')
ax.legend()
plt.show()
plt.close(fig)
def analyse_decline(args, rids, dpis, dprs, rds, non_rds):
print log.INFO, 'Analysing classification accuracies...'
# dpis = np.array(dpis)
# dprs = np.array(dprs)
# labels = np.array([1 if rid in rds else 0 for rid in rids])
dpis_rds = []
dpis_nonrds = []
dprs_rds = []
dprs_nonrds = []
for rid, dpi, dpr in zip(rids, dpis, dprs):
if rid in rds:
dpis_rds.append(dpi)
dprs_rds.append(dpr)
elif rid in non_rds:
dpis_nonrds.append(dpi)
dprs_nonrds.append(dpr)
dpis = np.concatenate((dpis_rds, dpis_nonrds))
dprs = np.concatenate((dprs_rds, dprs_nonrds))
labels = np.concatenate(
(np.ones(len(dpis_rds)), np.zeros(len(dpis_nonrds))))
# Assemble features
features = np.zeros((len(dpis), 2))
features[:, 0] = dpis
if args.estimate_dprs:
features[:, 1] = dprs
else:
# Copy DPIs as second features as LDA needs two features
features[:, 1] = dpis
features = preprocessing.scale(features)
acc, sens, spec = run_classification(args, features, labels)
print log.RESULT, '{0}-fold cross validation, RD vs. non-RD ACC={1:.2f}, SENS={2:.2f}, SPEC={3:.2f}'.format(
args.num_folds, acc, sens, spec)
if args.latex_file is not None:
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
filename = os.path.join(data_handler.get_eval_folder(),
args.latex_file)
print log.INFO, 'Writing classification results to {0}...'.format(
filename)
with open(filename, 'a') as latex_file:
latex_file.write(
'{0} & {1} & {2:.2f} & {3:.2f} & {4:.2f}\\\\\n'.format(
args.method, len(args.visits), acc, sens, spec))
def main():
# Collect data for test
data_handler = DataHandler.get_data_handler()
biomarkers = DataHandler.get_all_biomarker_names()
mean_changes = {}
for biomarker in biomarkers:
measurements = data_handler.get_measurements_as_dict(
visits=['bl', 'm12'], biomarkers=[biomarker], select_complete=True)
mean_changes_biomarker = {0.0: 0.0, 0.25: 0.0, 0.75: 0.0, 1.0: 0.0}
num_subjects = {0.0: 0, 0.25: 0, 0.75: 0, 1.0: 0}
for rid in measurements:
diagnosis = measurements[rid]['bl']['DX.scan']
value_bl = measurements[rid]['bl'][biomarker]
value_y1 = measurements[rid]['m12'][biomarker]
scantime_bl = measurements[rid]['bl']['scantime']
scantime_y1 = measurements[rid]['m12']['scantime']
change = (value_y1 - value_bl) / (scantime_y1 - scantime_bl)
mean_changes_biomarker[diagnosis] += change
num_subjects[diagnosis] += 1
mean_change_mci_ad = mean_changes_biomarker[
0.25] + mean_changes_biomarker[0.75] + mean_changes_biomarker[1.0]
num_subjects_mci_ad = num_subjects[0.25] + num_subjects[
0.75] + num_subjects[1.0]
for diagnosis in mean_changes_biomarker:
mean_changes_biomarker[diagnosis] /= num_subjects[diagnosis]
mean_changes_biomarker.update(
{0.66: mean_change_mci_ad / num_subjects_mci_ad})
mean_changes.update({biomarker: mean_changes_biomarker})
print log.RESULT, '{0} CN: {1}, (n={2})'.format(
biomarker, mean_changes_biomarker[0.0], num_subjects[0.0])
print log.RESULT, '{0} EMCI: {1}, (n={2})'.format(
biomarker, mean_changes_biomarker[0.25], num_subjects[0.25])
print log.RESULT, '{0} LMCI: {1}, (n={2})'.format(
biomarker, mean_changes_biomarker[0.75], num_subjects[0.75])
print log.RESULT, '{0} AD: {1}, (n={2})'.format(
biomarker, mean_changes_biomarker[1.0], num_subjects[1.0])
mean_changes_file = os.path.join(data_handler.get_eval_folder(),
'mean_changes.p')
pickle.dump(mean_changes, open(mean_changes_file, 'wb'))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-b', '--biomarkers', nargs=2, default=['D1', 'D2'], help='name of the biomarker to be plotted')
parser.add_argument('--plot_file', type=str, default=None, help='filename of the output file')
args = parser.parse_args()
# Collect data for test
data_handler = DataHandler.get_data_handler(biomarkers=args.biomarkers)
biomarkers = data_handler.get_biomarker_names()
measurements = data_handler.get_measurements_as_dict(biomarkers=biomarkers,
select_complete=True)
# Collect biomarker values
biomarkers_1 = []
biomarkers_2 = []
diagnoses = []
for rid in measurements:
for visit in measurements[rid]:
biomarkers_1.append(measurements[rid][visit][biomarkers[0]])
biomarkers_2.append(measurements[rid][visit][biomarkers[1]])
diagnoses.append(measurements[rid][visit]['DX.scan'])
diagnoses = np.array(diagnoses)
diagnoses[(0.25 <= diagnoses) & (diagnoses <= 0.75)] = 0.5
# Setup plot
fig, ax = plt.subplots()
pt.setup_axes(plt, ax)
ax.scatter(biomarkers_1, biomarkers_2, s=15.0, c=diagnoses, edgecolor='none',
vmin=0.0, vmax=1.0, cmap=pt.progression_cmap, alpha=0.25)
ax.set_xlabel(biomarkers[0])
ax.set_ylabel(biomarkers[1])
# Plot legend
rects = [mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_cn + (0.25,), linewidth=0),
mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_mci + (0.25,), linewidth=0),
mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_ad + (0.25,), linewidth=0)]
labels = ['CN', 'MCI', 'AD']
legend = ax.legend(rects, labels, fontsize=10, ncol=len(rects), loc='upper center', framealpha=0.9)
legend.get_frame().set_edgecolor((0.6, 0.6, 0.6))
# Draw or save the plot
plt.tight_layout()
if args.plot_file is not None:
plt.savefig(args.plot_file, transparent=True)
else:
plt.show()
plt.close(fig)
def print_to_latex(args, results_naive, results_model, num_subjects):
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
filename = os.path.join(data_handler.get_eval_folder(), args.latex_file)
with open(filename, 'a') as latex_file:
latex_file.write('{0} & {1} {2} & ${3:.2f}\pm{4:.2f}$ & ${5:.2f}$ & ${6:.2f}\pm{7:.2f}$ & ${8:.2f}$ & {9}\\\\\n'.format(
args.predict_biomarker,
args.method,
len(args.visits),
results_naive['MEAN'],
results_naive['STD'],
results_naive['CORR'],
results_model['MEAN'],
results_model['STD'],
results_model['CORR'],
num_subjects))
def main():
parser = argparse.ArgumentParser(
description='Estimate model curves for biomarkers using VGAM.')
parser.add_argument('-m',
'--method',
choices=DataHandler.get_method_choices(),
default='all',
help='the method to collect data for')
parser.add_argument('-b',
'--biomarkers',
nargs='+',
default=None,
help='name of the biomarker to be plotted')
parser.add_argument('-p',
'--phase',
default=None,
choices=DataHandler.get_phase_choices(),
help='the phase for which the model is to be trained')
parser.add_argument('-n',
'--nr_threads',
type=int,
default=1,
help='number of threads')
parser.add_argument('--min_visits',
type=int,
default=0,
help='the minimal number of visits')
parser.add_argument(
'--no_regression',
action='store_true',
default=False,
help='do not perform age regression of biomarker values')
parser.add_argument('--recompute_models',
action='store_true',
help='recompute the models with new samples')
args = parser.parse_args()
# Get the data files and biomarkers
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
# Estimate curves
# generate_csv_file(args, data_handler)
# print_gender_statistics(args, data_handler)
print_terminal_decline_statistics(args, data_handler)
def analyse_decline(args, rids, dpis, dprs, rds, non_rds):
print log.INFO, 'Analysing classification accuracies...'
# dpis = np.array(dpis)
# dprs = np.array(dprs)
# labels = np.array([1 if rid in rds else 0 for rid in rids])
dpis_rds = []
dpis_nonrds = []
dprs_rds = []
dprs_nonrds = []
for rid, dpi, dpr in zip(rids, dpis, dprs):
if rid in rds:
dpis_rds.append(dpi)
dprs_rds.append(dpr)
elif rid in non_rds:
dpis_nonrds.append(dpi)
dprs_nonrds.append(dpr)
dpis = np.concatenate((dpis_rds, dpis_nonrds))
dprs = np.concatenate((dprs_rds, dprs_nonrds))
labels = np.concatenate((np.ones(len(dpis_rds)), np.zeros(len(dpis_nonrds))))
# Assemble features
features = np.zeros((len(dpis), 2))
features[:, 0] = dpis
if args.estimate_dprs:
features[:, 1] = dprs
else:
# Copy DPIs as second features as LDA needs two features
features[:, 1] = dpis
features = preprocessing.scale(features)
acc, sens, spec = run_classification(args, features, labels)
print log.RESULT, '{0}-fold cross validation, RD vs. non-RD ACC={1:.2f}, SENS={2:.2f}, SPEC={3:.2f}'.format(args.num_folds, acc, sens, spec)
if args.latex_file is not None:
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
filename = os.path.join(data_handler.get_eval_folder(), args.latex_file)
print log.INFO, 'Writing classification results to {0}...'.format(filename)
with open(filename, 'a') as latex_file:
latex_file.write('{0} & {1} & {2:.2f} & {3:.2f} & {4:.2f}\\\\\n'.format(
args.method,
len(args.visits),
acc, sens, spec))
def main():
# Parse input arguments
parser = argparse.ArgumentParser()
parser.add_argument('--estimate_dpr',
action='store_true',
help='recompute the dpis estimations')
parser.add_argument('--samples_file',
type=str,
default='measurements_sample.csv',
help='recompute the dpis estimations')
args = parser.parse_args()
# Read the measurements as dict from the csv file
measurements, biomarkers = read_measurements_from_cvs(args.samples_file)
visits = measurements[0].keys()
# Get estimates
data_handler = DataHandler.get_data_handler(method='all',
biomarkers=biomarkers,
phase='joint')
# Setup model
model = MultiBiomarkerProgressionModel()
for biomarker in biomarkers:
model_file = data_handler.get_model_file(biomarker)
model.add_model(biomarker, model_file)
fitter = ModelFitter(model)
# Estimate dpis (and dprs) and save data
if args.estimate_dpr:
rids, diagnoses, dpis, dprs = estimate_dpis_dprs(measurements,
visits,
fitter,
phase='joint')
else:
rids, diagnoses, dpis = estimate_dpis(measurements,
visits,
fitter,
phase='joint')
dprs = np.ones(len(dpis)).tolist()
# Plot the models with the fitted samples
for biomarker in biomarkers:
plot_biomarker(data_handler, biomarker, measurements, dpis[0], dprs[0])
def main():
parser = argparse.ArgumentParser(description='Estimate model curves for biomarkers using VGAM.')
parser.add_argument('-m', '--method', choices=DataHandler.get_method_choices(), default='all', help='the method to collect data for')
parser.add_argument('-b', '--biomarkers', nargs='+', default=None, help='name of the biomarker to be plotted')
parser.add_argument('-p', '--phase', default=None, choices=DataHandler.get_phase_choices(), help='the phase for which the model is to be trained')
parser.add_argument('-n', '--nr_threads', type=int, default=1, help='number of threads')
parser.add_argument('--min_visits', type=int, default=0, help='the minimal number of visits')
parser.add_argument('--no_regression', action='store_true', default=False, help='do not perform age regression of biomarker values')
parser.add_argument('--recompute_models', action='store_true', help='recompute the models with new samples')
args = parser.parse_args()
# Get the data files and biomarkers
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
# Estimate curves
# generate_csv_file(args, data_handler)
# print_gender_statistics(args, data_handler)
print_terminal_decline_statistics(args, data_handler)
def main():
# Collect data for test
data_handler = DataHandler.get_data_handler()
biomarkers = DataHandler.get_all_biomarker_names()
mean_changes = {}
for biomarker in biomarkers:
measurements = data_handler.get_measurements_as_dict(visits=['bl', 'm12'],
biomarkers=[biomarker],
select_complete=True)
mean_changes_biomarker = {0.0: 0.0, 0.25: 0.0, 0.75: 0.0, 1.0: 0.0}
num_subjects = {0.0: 0, 0.25: 0, 0.75: 0, 1.0: 0}
for rid in measurements:
diagnosis = measurements[rid]['bl']['DX.scan']
value_bl = measurements[rid]['bl'][biomarker]
value_y1 = measurements[rid]['m12'][biomarker]
scantime_bl = measurements[rid]['bl']['scantime']
scantime_y1 = measurements[rid]['m12']['scantime']
change = (value_y1 - value_bl) / (scantime_y1 - scantime_bl)
mean_changes_biomarker[diagnosis] += change
num_subjects[diagnosis] += 1
mean_change_mci_ad = mean_changes_biomarker[0.25] + mean_changes_biomarker[0.75] + mean_changes_biomarker[1.0]
num_subjects_mci_ad = num_subjects[0.25] + num_subjects[0.75] + num_subjects[1.0]
for diagnosis in mean_changes_biomarker:
mean_changes_biomarker[diagnosis] /= num_subjects[diagnosis]
mean_changes_biomarker.update({0.66: mean_change_mci_ad / num_subjects_mci_ad})
mean_changes.update({biomarker: mean_changes_biomarker})
print log.RESULT, '{0} CN: {1}, (n={2})'.format(biomarker, mean_changes_biomarker[0.0], num_subjects[0.0])
print log.RESULT, '{0} EMCI: {1}, (n={2})'.format(biomarker, mean_changes_biomarker[0.25], num_subjects[0.25])
print log.RESULT, '{0} LMCI: {1}, (n={2})'.format(biomarker, mean_changes_biomarker[0.75], num_subjects[0.75])
print log.RESULT, '{0} AD: {1}, (n={2})'.format(biomarker, mean_changes_biomarker[1.0], num_subjects[1.0])
mean_changes_file = os.path.join(data_handler.get_eval_folder(), 'mean_changes.p')
pickle.dump(mean_changes, open(mean_changes_file, 'wb'))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m', '--method', choices=DataHandler.get_method_choices(), default='all', help='the method to collect data for')
parser.add_argument('-b', '--biomarkers', nargs='+', default=None, help='name of the biomarker to be plotted')
parser.add_argument('-p', '--phase', default='mciad', choices=DataHandler.get_phase_choices(), help='the phase for which the model is to be trained')
parser.add_argument('-e', '--extrapolator', type=str, choices=['lin', 'sqrt', 'exp'], default='exp', help='the type of extrapolator')
args = parser.parse_args()
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
biomarkers = data_handler.get_biomarker_names()
if args.method == 'joint':
offsets = np.linspace(500, 3000, 26)
else:
offsets = np.linspace(-1000, 1000, 21)
all_diffs = np.zeros((len(offsets), len(biomarkers)))
for i, biomarker in enumerate(biomarkers):
diffs = get_model_differences(args, data_handler, biomarker, offsets)
all_diffs[:, i] = diffs
print biomarker, offsets[np.argmin(diffs)]
optimum_index = np.argmin(np.mean(all_diffs, axis=1))
print 'all', offsets[optimum_index]
mins = all_diffs[optimum_index, :] # np.min(all_diffs, axis=0)
indices = np.argsort(mins)
for i in indices:
print biomarkers[i], mins[i]
fig = plt.figure()
ax1 = plt.subplot(1, 1, 1)
ax1.plot(offsets, all_diffs, color='r')
ax1.plot(offsets, np.mean(all_diffs, axis=1), color='b')
plt.show()
plt.close(fig)
def get_rfds(args, rids, diagnoses, dpis, dprs):
data_handler = DataHandler.get_data_handler()
measurements = data_handler.get_measurements_as_dict(
visits=['bl', 'm24'],
biomarkers=['FAQ'],
select_complete=True,
no_regression=True)
rfds = set()
non_rfds = set()
for rid in rids:
if rid in measurements:
faq_bl = measurements[rid]['bl']['FAQ']
faq_m24 = measurements[rid]['m24']['FAQ']
rcd = (faq_m24 - faq_bl) >= 10
if rcd:
rfds.add(rid)
else:
non_rfds.add(rid)
print log.RESULT, 'Selected {0} subjects with rapid functional decline (RFD).'.format(len(rfds))
print log.RESULT, 'Selected {0} subjects without rapid functional decline (non-RFD).'.format(len(non_rfds))
return rfds, non_rfds
def get_rcds(args, rids, diagnoses, dpis, dprs):
data_handler = DataHandler.get_data_handler()
measurements = data_handler.get_measurements_as_dict(
visits=['bl', 'm24'],
biomarkers=['MMSE'],
select_complete=True,
no_regression=True)
rcds = set()
non_rcds = set()
for rid in rids:
if rid in measurements:
mmse_bl = measurements[rid]['bl']['MMSE']
mmse_m24 = measurements[rid]['m24']['MMSE']
rcd = (mmse_bl - mmse_m24) >= 8
if rcd:
rcds.add(rid)
else:
non_rcds.add(rid)
print log.RESULT, 'Selected {0} subjects with rapid cognitive decline (RCD).'.format(len(rcds))
print log.RESULT, 'Selected {0} subjects without rapid cognitive decline (non-RCD).'.format(len(non_rcds))
return rcds, non_rcds
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m', '--method', choices=DataHandler.get_method_choices(), default='all', help='the method to collect data for')
parser.add_argument('-b', '--biomarkers', nargs='+', default=None, help='name of the biomarker to be plotted')
parser.add_argument('-p', '--phase', default=None, choices=DataHandler.get_phase_choices(), help='the phase for which the model is to be trained')
parser.add_argument('-n', '--nr_threads', type=int, default=4, help='number of threads')
parser.add_argument('--recompute_metric', action='store_true', help='recompute the metric')
parser.add_argument('--value_samples', type=int, default=100, help='the number of values samples')
parser.add_argument('--progress_samples', type=int, default=50, help='the number of progress samples')
parser.add_argument('--quantiles', type=float, nargs=2, default=[0.01, 0.99], help='the quantiles for the interval computation')
parser.add_argument('--metric', type=str, default='cover', help='the metric used for the evaluation')
args = parser.parse_args()
# Collect data for test
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
# Compute error for each biomarker
biomarkers = data_handler.get_biomarker_names()
evaluation_function = evaluate_biomarker_cover if args.metric == 'cover' else evaluate_biomarker_disc
jl.Parallel(n_jobs=args.nr_threads)(jl.delayed(evaluation_function)(args, data_handler, biomarker) for biomarker in biomarkers)
sort_biomarkers(args, data_handler, biomarkers)
def get_rfds(args, rids, diagnoses, dpis, dprs):
data_handler = DataHandler.get_data_handler()
measurements = data_handler.get_measurements_as_dict(visits=['bl', 'm24'],
biomarkers=['FAQ'],
select_complete=True,
no_regression=True)
rfds = set()
non_rfds = set()
for rid in rids:
if rid in measurements:
faq_bl = measurements[rid]['bl']['FAQ']
faq_m24 = measurements[rid]['m24']['FAQ']
rcd = (faq_m24 - faq_bl) >= 10
if rcd:
rfds.add(rid)
else:
non_rfds.add(rid)
print log.RESULT, 'Selected {0} subjects with rapid functional decline (RFD).'.format(
len(rfds))
print log.RESULT, 'Selected {0} subjects without rapid functional decline (non-RFD).'.format(
len(non_rfds))
return rfds, non_rfds
def get_rcds(args, rids, diagnoses, dpis, dprs):
data_handler = DataHandler.get_data_handler()
measurements = data_handler.get_measurements_as_dict(visits=['bl', 'm24'],
biomarkers=['MMSE'],
select_complete=True,
no_regression=True)
rcds = set()
non_rcds = set()
for rid in rids:
if rid in measurements:
mmse_bl = measurements[rid]['bl']['MMSE']
mmse_m24 = measurements[rid]['m24']['MMSE']
rcd = (mmse_bl - mmse_m24) >= 8
if rcd:
rcds.add(rid)
else:
non_rcds.add(rid)
print log.RESULT, 'Selected {0} subjects with rapid cognitive decline (RCD).'.format(
len(rcds))
print log.RESULT, 'Selected {0} subjects without rapid cognitive decline (non-RCD).'.format(
len(non_rcds))
return rcds, non_rcds
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m',
'--method',
choices=DataHandler.get_method_choices(),
default='all',
help='the method to collect data for')
parser.add_argument('-b',
'--biomarkers',
nargs='+',
default=None,
help='name of the biomarker to be plotted')
parser.add_argument('-p',
'--phase',
default=None,
choices=DataHandler.get_phase_choices(),
help='the phase for which the model is to be trained')
parser.add_argument('--save_plots',
action='store_true',
default=False,
help='save the plots with a default filename')
args = parser.parse_args()
# Collect data for test
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
biomarkers = data_handler.get_biomarker_names()
measurements = data_handler.get_measurements_as_dict(
visits=['bl', 'm12'],
biomarkers=biomarkers,
select_training_set=True,
select_complete=True)
# Setup plotting folder
eval_folder = DataHandler.make_dir(data_handler.get_eval_folder(),
'quants')
# Process all biomarkers
for biomarker in biomarkers:
print log.INFO, 'Generating quantile correlation plot for {0}...'.format(
biomarker)
model_file = data_handler.get_model_file(biomarker)
pm = ProgressionModel(biomarker, model_file)
q_file = os.path.join(eval_folder, '{0}.p'.format(biomarker))
if os.path.isfile(q_file):
(q_bl, q_m12) = pickle.load(open(q_file, 'rb'))
else:
q_bl = []
q_m12 = []
for rid in measurements:
val_bl = measurements[rid]['bl'][biomarker]
val_m12 = measurements[rid]['m12'][biomarker]
p_bl = measurements[rid]['bl']['progress']
p_m12 = measurements[rid]['m12']['progress']
q_bl.append(pm.approximate_quantile(p_bl, val_bl))
q_m12.append(pm.approximate_quantile(p_m12, val_m12))
pickle.dump((q_bl, q_m12), open(q_file, 'wb'))
# Setup plot
fig, axs = plt.subplots(1, 2)
plt.suptitle('Correlation between bl and m12 quantiles')
# Plot 1
ax = axs[0]
pt.setup_axes(plt, ax, yspine=True)
ax.set_xlabel('Quantile bl')
ax.set_ylabel('Quantile m12')
ax.scatter(q_bl, q_m12, edgecolor='none', s=25.0, alpha=0.5)
# Plot 2
q_bl = np.array(q_bl)
q_m12 = np.array(q_m12)
errors = q_bl - q_m12
loc, scale = norm.fit(errors, floc=0.0)
ax = axs[1]
pt.setup_axes(plt, ax)
ax.set_xlabel('Difference bl to m12')
ax.set_ylabel('Probability')
ax.set_xlim(-1.05, 1.05)
ax.hist(errors, bins=15, normed=True, histtype='stepfilled', alpha=0.3)
x = np.linspace(-1.0, 1.0, 100)
ax.plot(x, norm.pdf(x, loc=loc, scale=scale), color='k')
# Draw or save the plot
plt.tight_layout()
if args.save_plots:
plot_file = os.path.join(eval_folder, '{0}.pdf'.format(biomarker))
plt.savefig(plot_file, transparent=True)
else:
plt.show()
plt.close(fig)
def get_fitting_data(args, data_handler_joint):
biomarkers = data_handler_joint.get_biomarker_names()
offsets = range(args.search_range[0], args.search_range[1], args.search_range[2])
errors_file = os.path.join(data_handler_joint.get_eval_folder(),
'offset_errors_{0}.p'.format(args.extrapolator))
if os.path.isfile(errors_file) and not args.recompute_errors:
print log.INFO, 'Reading errors estimations from file {0}...'.format(errors_file)
(errors, descriminativeness, overlap) = pickle.load(open(errors_file, 'rb'))
else:
data_handler_1 = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase='cnmci')
data_handler_2 = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase='mciad')
errors = np.zeros((len(biomarkers), len(offsets)))
descriminativeness = np.zeros(len(biomarkers))
overlap = []
for i, biomarker in enumerate(biomarkers):
# Get error matrix for all biomarkers and offsets
model_file_1 = data_handler_1.get_model_file(biomarker)
model_file_2 = data_handler_2.get_model_file(biomarker)
if os.path.isfile(model_file_1) and os.path.isfile(model_file_2):
print log.INFO, 'Analysing {0}...'.format(biomarker)
# Get discriminativeness for all biomarkers as a scaling factor
eval_file_1 = model_file_1.replace('.csv', '_eval_cover.csv')
eval_file_2 = model_file_2.replace('.csv', '_eval_cover.csv')
if os.path.isfile(eval_file_1) and os.path.isfile(eval_file_2):
descriminate_1 = np.mean(mlab.csv2rec(eval_file_1)['error'])
descriminate_2 = np.mean(mlab.csv2rec(eval_file_2)['error'])
descriminativeness[i] = 0.5 * (descriminate_1 + descriminate_2)
else:
print log.WARNING, 'Evaluation file missing for {0}'.format(biomarker)
continue
# Initialise models
model_1 = ProgressionModel(biomarker, model_file_1, extrapolator=args.extrapolator)
model_2 = ProgressionModel(biomarker, model_file_2, extrapolator=args.extrapolator)
# Assemble errors for each offset
min_val_1, max_val_1 = model_1.get_value_range([0.1, 0.9])
min_val_2, max_val_2 = model_2.get_value_range([0.1, 0.9])
values = np.linspace(min(min_val_1, min_val_2), max(max_val_1, max_val_2), 250)
values_delta = (values.max() - values.min()) / len(values)
for j, offset in enumerate(offsets):
dens_11 = np.array(model_1.get_density_distribution(values, offset + model_2.min_progress))
dens_12 = np.array(model_2.get_density_distribution(values, model_2.min_progress))
dens_21 = np.array(model_1.get_density_distribution(values, model_1.max_progress))
dens_22 = np.array(model_2.get_density_distribution(values, -offset + model_1.max_progress))
errors[i, j] = 0.5 * values_delta * (np.sum(np.abs(dens_11 - dens_12)) + np.sum(np.abs(dens_21 - dens_22)))
# Get overlap
overlap.append(model_1.max_progress - model_2.min_progress)
overlap = np.mean(overlap)
print log.INFO, 'Saving errors to file {0}...'.format(errors_file)
pickle.dump((errors, descriminativeness, overlap), open(errors_file, 'wb'))
return biomarkers, offsets, errors, descriminativeness, overlap
def get_progress_estimates(visits,
method=None, biomarkers=None, phase=None,
recompute_estimates=False,
estimate_dprs=False, consistent_data=False,
select_training_set=False, select_test_set=False):
# Get data handler and biomarker names
data_handler = DataHandler.get_data_handler(method=method,
biomarkers=biomarkers,
phase=phase)
# Get filename
estimates_file_trunk = 'estimate_dpi_dpr_with_{0}_{1}.p' if estimate_dprs else 'estimate_dpi_with_{0}_{1}.p'
if biomarkers is None:
estimates_file_basename = estimates_file_trunk.format(method, '_'.join(visits))
else:
biomarkers_string = '_'.join(biomarkers).replace(' ', '_')
estimates_file_basename = estimates_file_trunk.format(biomarkers_string, '_'.join(visits))
estimates_file = os.path.join(data_handler.get_eval_folder(), estimates_file_basename)
# Read if estimates exist, else recompute
if os.path.isfile(estimates_file) and not recompute_estimates:
# Read test results from file
print log.INFO, 'Reading DPI{0} estimations from {1}...'.format('\DPR' if estimate_dprs else '', estimates_file)
(rids, diagnoses, dpis, dprs, mean_min, mean_max) = pickle.load(open(estimates_file, 'rb'))
else:
# Collect data for test
biomarkers = data_handler.get_biomarker_names()
measurements = data_handler.get_measurements_as_dict(visits=['bl', 'm12', 'm24'],
biomarkers=biomarkers,
select_complete=True)
# Setup model
model = MultiBiomarkerProgressionModel()
for biomarker in biomarkers:
model_file = data_handler.get_model_file(biomarker)
model.add_model(biomarker, model_file)
fitter = ModelFitter(model)
# Calculate mean and max progress
mean_min = model.get_mean_min_progress()
mean_max = model.get_mean_max_progress()
# Estimate dpis (and dprs) and save data
if not estimate_dprs or len(visits) == 1:
if estimate_dprs and len(visits) == 1:
print log.WARNING, 'Only one visit, cannot estimate DPR (setting to one)'
rids, diagnoses, dpis = estimate_dpis(measurements, visits, fitter, phase=phase)
dprs = np.ones(len(dpis)).tolist()
else:
rids, diagnoses, dpis, dprs = estimate_dpis_dprs(measurements, visits, fitter, phase=phase)
print log.INFO, 'Saving DPI{0} estimations to {1}...'.format('\DPR' if estimate_dprs else '', estimates_file)
pickle.dump((rids, diagnoses, dpis, dprs, mean_min, mean_max), open(estimates_file, 'wb'))
# Reduce to consistent data sets with bl, m12 and m24 samples
if consistent_data or select_training_set or select_test_set:
consistent_method = 'all' if consistent_data else method
consistent_data_handler = DataHandler.get_data_handler(method=consistent_method)
consistent_measurements = consistent_data_handler.get_measurements_as_dict(
visits=['bl', 'm12', 'm24'],
select_training_set=select_training_set,
select_test_set=select_test_set,
select_complete=True,
no_regression=True)
consistent_rids = []
consistent_diagnoses = []
consistent_dpis = []
consistent_dprs = []
for i, rid in enumerate(rids):
if rid in consistent_measurements:
consistent_rids.append(rid)
consistent_diagnoses.append(diagnoses[i])
consistent_dpis.append(dpis[i])
consistent_dprs.append(dprs[i])
rids = consistent_rids
diagnoses = consistent_diagnoses
dpis = consistent_dpis
dprs = consistent_dprs
print log.RESULT, 'Selected {0} consistent subjects.'.format(len(dpis))
# Return results
return rids, diagnoses, dpis, dprs, mean_min, mean_max
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m', '--method', choices=DataHandler.get_method_choices(), default='all', help='the method to collect data for')
parser.add_argument('-b', '--biomarkers', nargs='+', default=None, help='name of the biomarker to be plotted')
parser.add_argument('-p', '--phase', default=None, choices=DataHandler.get_phase_choices(), help='the phase for which the model is to be trained')
parser.add_argument('--save_plots', action='store_true', default=False, help='save the plots with a default filename')
args = parser.parse_args()
# Collect data for test
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
biomarkers = data_handler.get_biomarker_names()
measurements = data_handler.get_measurements_as_dict(visits=['bl', 'm12'],
biomarkers=biomarkers,
select_training_set=True,
select_complete=True)
# Setup plotting folder
eval_folder = DataHandler.make_dir(data_handler.get_eval_folder(), 'quants')
# Process all biomarkers
for biomarker in biomarkers:
print log.INFO, 'Generating quantile correlation plot for {0}...'.format(biomarker)
model_file = data_handler.get_model_file(biomarker)
pm = ProgressionModel(biomarker, model_file)
q_file = os.path.join(eval_folder, '{0}.p'.format(biomarker))
if os.path.isfile(q_file):
(q_bl, q_m12) = pickle.load(open(q_file, 'rb'))
else:
q_bl = []
q_m12 = []
for rid in measurements:
val_bl = measurements[rid]['bl'][biomarker]
val_m12 = measurements[rid]['m12'][biomarker]
p_bl = measurements[rid]['bl']['progress']
p_m12 = measurements[rid]['m12']['progress']
q_bl.append(pm.approximate_quantile(p_bl, val_bl))
q_m12.append(pm.approximate_quantile(p_m12, val_m12))
pickle.dump((q_bl, q_m12), open(q_file, 'wb'))
# Setup plot
fig, axs = plt.subplots(1, 2)
plt.suptitle('Correlation between bl and m12 quantiles')
# Plot 1
ax = axs[0]
pt.setup_axes(plt, ax, yspine=True)
ax.set_xlabel('Quantile bl')
ax.set_ylabel('Quantile m12')
ax.scatter(q_bl, q_m12, edgecolor='none', s=25.0, alpha=0.5)
# Plot 2
q_bl = np.array(q_bl)
q_m12 = np.array(q_m12)
errors = q_bl - q_m12
loc, scale = norm.fit(errors, floc=0.0)
ax = axs[1]
pt.setup_axes(plt, ax)
ax.set_xlabel('Difference bl to m12')
ax.set_ylabel('Probability')
ax.set_xlim(-1.05, 1.05)
ax.hist(errors, bins=15, normed=True, histtype='stepfilled', alpha=0.3)
x = np.linspace(-1.0, 1.0, 100)
ax.plot(x, norm.pdf(x, loc=loc, scale=scale), color='k')
# Draw or save the plot
plt.tight_layout()
if args.save_plots:
plot_file = os.path.join(eval_folder, '{0}.pdf'.format(biomarker))
plt.savefig(plot_file, transparent=True)
else:
plt.show()
plt.close(fig)
def plot_dpi_estimates(args, dpis, diagnoses, mean_min, mean_max):
print log.INFO, 'Plotting estimates...'
test_dpi_min, test_dpi_max, _ = ModelFitter.get_test_dpi_range(args.phase)
dpi_range = float(test_dpi_max - test_dpi_min)
dpi_factor = float(args.plot_steps) / dpi_range
# Setup plot
fig, ax = plt.subplots(figsize=(6, 2))
biomarkers_str = args.method if args.biomarkers is None else ', '.join(
args.biomarkers)
ax.set_title('DP estimation using {0} at {1}'.format(
biomarkers_str, ', '.join(args.visits)))
ax.spines['left'].set_position(('outward', 10))
ax.spines['bottom'].set_position(('outward', 10))
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
xticks = np.linspace(0, args.plot_steps, 7)
ax.set_xticks(xticks)
ax.set_xticklabels(
[int(float(tick) / dpi_factor + test_dpi_min) for tick in xticks])
# Compute matrix
diagnosis_indices = {0.0: 0, 0.25: 1, 0.5: 1, 0.75: 2, 1.0: 3}
matrix = np.zeros((4, args.plot_steps + 1))
for dpi, diag in zip(dpis, diagnoses):
row = diagnosis_indices[diag]
dpi_index = round((dpi - test_dpi_min) * dpi_factor)
matrix[row, dpi_index] += 1.0
# Draw annotations
dpis = np.array(dpis)
diagnoses = np.array(diagnoses)
medians = []
q25 = []
q75 = []
for diag in [0.0, 0.25, 0.75, 1.0]:
row = diagnosis_indices[diag]
matrix[row] /= np.sum(matrix[row])
indices = np.where(diagnoses == diag)
median = np.median(dpis[indices])
medians.append((median - test_dpi_min) * dpi_factor)
q25.append((median - np.percentile(dpis[indices], 25)) * dpi_factor)
q75.append((np.percentile(dpis[indices], 75) - median) * dpi_factor)
if args.plot_lines:
ax.set_ylim(-0.01, 0.36)
sample_cmap = cmx.ScalarMappable(norm=colors.Normalize(0.0, 1.0),
cmap=plt.get_cmap(
pt.progression_cmap))
for diag in [0.0, 0.25, 0.75, 1.0]:
row = diagnosis_indices[diag]
plt.plot(matrix[row], color=sample_cmap.to_rgba(diag))
else:
ax.set_yticks([0, 1, 2, 3])
ax.set_yticklabels(['CN', 'EMCI', 'LMCI', 'AD'])
cmap = plt.get_cmap('jet') if args.plot_cmap_jet else plt.get_cmap(
'Greys')
bar_color = 'w' if args.plot_cmap_jet else 'r'
plt.errorbar(medians, [0, 1, 2, 3],
xerr=[q25, q75],
fmt='none',
ecolor=bar_color,
elinewidth=2,
capsize=4,
capthick=2)
plt.plot(medians, [0, 1, 2, 3],
linestyle='',
color=bar_color,
marker='|',
markersize=15,
markeredgewidth=2)
plt.imshow(matrix, cmap=cmap, interpolation='nearest')
plt.axvline((mean_min - test_dpi_min) * dpi_factor,
color='k',
linestyle=':',
alpha=0.6)
plt.axvline((mean_max - test_dpi_min) * dpi_factor,
color='k',
linestyle=':',
alpha=0.6)
plt.axvline((0.0 - test_dpi_min) * dpi_factor,
color='k',
linestyle='-',
alpha=0.6)
if args.phase == 'joint':
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
plt.axvline(
(data_handler.get_model_offset() - test_dpi_min) * dpi_factor,
color='k',
linestyle='-',
alpha=0.6)
# Draw or save the plot
plt.tight_layout()
if args.plot_file is not None:
plt.savefig(args.plot_file, transparent=True)
else:
plt.show()
plt.close(fig)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-b',
'--biomarkers',
nargs=2,
default=['D1', 'D2'],
help='name of the biomarker to be plotted')
parser.add_argument('--plot_file',
type=str,
default=None,
help='filename of the output file')
args = parser.parse_args()
# Collect data for test
data_handler = DataHandler.get_data_handler(biomarkers=args.biomarkers)
biomarkers = data_handler.get_biomarker_names()
measurements = data_handler.get_measurements_as_dict(biomarkers=biomarkers,
select_complete=True)
# Collect biomarker values
biomarkers_1 = []
biomarkers_2 = []
diagnoses = []
for rid in measurements:
for visit in measurements[rid]:
biomarkers_1.append(measurements[rid][visit][biomarkers[0]])
biomarkers_2.append(measurements[rid][visit][biomarkers[1]])
diagnoses.append(measurements[rid][visit]['DX.scan'])
diagnoses = np.array(diagnoses)
diagnoses[(0.25 <= diagnoses) & (diagnoses <= 0.75)] = 0.5
# Setup plot
fig, ax = plt.subplots()
pt.setup_axes(plt, ax)
ax.scatter(biomarkers_1,
biomarkers_2,
s=15.0,
c=diagnoses,
edgecolor='none',
vmin=0.0,
vmax=1.0,
cmap=pt.progression_cmap,
alpha=0.25)
ax.set_xlabel(biomarkers[0])
ax.set_ylabel(biomarkers[1])
# Plot legend
rects = [
mpl.patches.Rectangle((0, 0),
1,
1,
fc=pt.color_cn + (0.25, ),
linewidth=0),
mpl.patches.Rectangle((0, 0),
1,
1,
fc=pt.color_mci + (0.25, ),
linewidth=0),
mpl.patches.Rectangle((0, 0),
1,
1,
fc=pt.color_ad + (0.25, ),
linewidth=0)
]
labels = ['CN', 'MCI', 'AD']
legend = ax.legend(rects,
labels,
fontsize=10,
ncol=len(rects),
loc='upper center',
framealpha=0.9)
legend.get_frame().set_edgecolor((0.6, 0.6, 0.6))
# Draw or save the plot
plt.tight_layout()
if args.plot_file is not None:
plt.savefig(args.plot_file, transparent=True)
else:
plt.show()
plt.close(fig)
def get_biomarker_predictions(visits, predict_biomarker,
method=None, biomarkers=None, phase=None,
recompute_estimates=False, recompute_predictions=False, estimate_dprs=False,
select_test_set=False, consistent_data=False, exclude_cn=False,
use_last_visit=False, naive_use_diagnosis=False):
# Get prediction file
data_handler = DataHandler.get_data_handler(method=method,
biomarkers=biomarkers,
phase=phase)
predict_biomarker_str = predict_biomarker.replace(' ', '_')
predict_file_trunk = 'predict_{0}_with_dpr_{1}_{2}{3}.p' if estimate_dprs else 'predict_{0}_with_{1}_{2}{3}.p'
if biomarkers is None:
predict_file_basename = predict_file_trunk.format(predict_biomarker_str,
method, '_'.join(visits),
'_last' if use_last_visit else '')
else:
estimate_biomarkers_string = '_'.join(biomarkers).replace(' ', '_')
predict_file_basename = predict_file_trunk.format(predict_biomarker_str,
estimate_biomarkers_string,
'_'.join(visits),
'_last' if use_last_visit else '')
prediction_file = os.path.join(data_handler.get_eval_folder(), predict_file_basename)
# Read if predictions exist, else recompute
if os.path.isfile(prediction_file) and not recompute_predictions:
# Read biomarker predictions from file
print log.INFO, 'Reading {0} predictions from {1}...'.format(predict_biomarker, prediction_file)
(rids, diagnoses, values_observed, values_naive, values_model) = pickle.load(open(prediction_file, 'rb'))
else:
predict_visit = get_predicted_visit(visits)
print log.INFO, 'Predicting {0} at {1}...'.format(predict_biomarker, predict_visit)
# Get mean changes from file
mean_changes_file = os.path.join(data_handler.get_eval_folder(), 'mean_changes.p')
if not os.path.isfile(mean_changes_file):
print log.ERROR, 'Mean changes unknown, run misc/compute_mean_biomarker_changes.py first!'
mean_changes = pickle.load(open(mean_changes_file, 'rb'))
# Get DPI estimates
rids_all, diagnoses_all, dpis, dprs, _, _ = get_progress_estimates(visits,
method=method,
biomarkers=biomarkers,
phase=phase,
recompute_estimates=recompute_estimates,
estimate_dprs=estimate_dprs,
select_test_set=select_test_set,
consistent_data=consistent_data)
# Collect biomarker data for test
measurements = data_handler.get_measurements_as_dict(visits=visits + [predict_visit],
biomarkers=[predict_biomarker],
select_test_set=select_test_set,
select_complete=True)
model = ProgressionModel(predict_biomarker, data_handler.get_model_file(predict_biomarker))
print log.INFO, 'Predicting {0} for {1}'.format(predict_biomarker, predict_visit)
rids = []
diagnoses = []
values_observed = []
values_model = []
values_naive = []
for rid, diagnosis, dpi, dpr in zip(rids_all, diagnoses_all, dpis, dprs):
if rid in measurements:
# Get real biomarker value value at next visit
scantime_first_visit = measurements[rid][visits[0]]['scantime']
scantime_next_visit = measurements[rid][predict_visit]['scantime']
progress_next_visit = ModelFitter.scantime_to_progress(scantime_next_visit, scantime_first_visit, dpi, dpr)
value_observed = measurements[rid][predict_visit][predict_biomarker]
values_observed.append(value_observed)
# Predict biomarker value value at next visit
if use_last_visit:
value = measurements[rid][visits[-1]][predict_biomarker]
scantime = measurements[rid][visits[-1]]['scantime']
progress = ModelFitter.scantime_to_progress(scantime, scantime_first_visit, dpi, dpr)
mean_quantile = model.approximate_quantile(progress, value)
else:
mean_quantile = 0.0
for visit in visits:
value = measurements[rid][visit][predict_biomarker]
scantime = measurements[rid][visit]['scantime']
progress = ModelFitter.scantime_to_progress(scantime, scantime_first_visit, dpi, dpr)
mean_quantile += model.approximate_quantile(progress, value)
mean_quantile /= len(visits)
value_model = model.get_value_at_quantile(progress_next_visit, mean_quantile)
values_model.append(value_model)
# Predict biomarker value naively
if naive_use_diagnosis:
mean_change = mean_changes[predict_biomarker][diagnosis]
else:
mean_change = mean_changes[predict_biomarker][0.66]
if use_last_visit:
x = measurements[rid][visits[-1]]['scantime']
y = measurements[rid][visits[-1]][predict_biomarker]
intercept = -(mean_change * x - y)
else:
x = np.zeros(len(visits))
y = np.zeros(len(visits))
for i, visit in enumerate(visits):
x[i] = measurements[rid][visit]['scantime']
y[i] = measurements[rid][visit][predict_biomarker]
intercept = -np.sum(mean_change * x - y) / len(x)
value_naive = intercept + mean_change * measurements[rid][predict_visit]['scantime']
values_naive.append(value_naive)
# Plot estimates
plot = True
if plot and diagnosis > 0.0 and dpr > 0.0:
plot_predictions(predict_biomarker, model, visits, measurements[rid], dpi, dpr,
value_model, value_naive,
mean_quantile, mean_change, intercept, rid)
# Append rid and diagnosis
rids.append(rid)
diagnoses.append(diagnosis)
# Print result
print log.RESULT, '{0} for subject {1}: Observed: {2}, Naive {3}, Model: {4}'.format(predict_biomarker, rid, value_observed, value_naive, value_model)
# Save results
print log.INFO, 'Saving {0} predictions to {1}...'.format(predict_biomarker, prediction_file)
pickle.dump((rids, diagnoses, values_observed, values_naive, values_model), open(prediction_file, 'wb'))
rids = np.array(rids)
diagnoses = np.array(diagnoses)
values_observed = np.array(values_observed)
values_naive = np.array(values_naive)
values_model = np.array(values_model)
# Exclude healthy subjects
if exclude_cn:
indices = np.where(diagnoses > 0.25)
rids = rids[indices]
diagnoses = diagnoses[indices]
values_observed = values_observed[indices]
values_naive = values_naive[indices]
values_model = values_model[indices]
return rids, diagnoses, values_observed, values_naive, values_model
def get_progress_estimates(visits,
method=None,
biomarkers=None,
phase=None,
recompute_estimates=False,
estimate_dprs=False,
consistent_data=False,
select_training_set=False,
select_test_set=False):
# Get data handler and biomarker names
data_handler = DataHandler.get_data_handler(method=method,
biomarkers=biomarkers,
phase=phase)
# Get filename
estimates_file_trunk = 'estimate_dpi_dpr_with_{0}_{1}.p' if estimate_dprs else 'estimate_dpi_with_{0}_{1}.p'
if biomarkers is None:
estimates_file_basename = estimates_file_trunk.format(
method, '_'.join(visits))
else:
biomarkers_string = '_'.join(biomarkers).replace(' ', '_')
estimates_file_basename = estimates_file_trunk.format(
biomarkers_string, '_'.join(visits))
estimates_file = os.path.join(data_handler.get_eval_folder(),
estimates_file_basename)
# Read if estimates exist, else recompute
if os.path.isfile(estimates_file) and not recompute_estimates:
# Read test results from file
print log.INFO, 'Reading DPI{0} estimations from {1}...'.format(
'\DPR' if estimate_dprs else '', estimates_file)
(rids, diagnoses, dpis, dprs, mean_min,
mean_max) = pickle.load(open(estimates_file, 'rb'))
else:
# Collect data for test
biomarkers = data_handler.get_biomarker_names()
measurements = data_handler.get_measurements_as_dict(
visits=['bl', 'm12', 'm24'],
biomarkers=biomarkers,
select_complete=True)
# Setup model
model = MultiBiomarkerProgressionModel()
for biomarker in biomarkers:
model_file = data_handler.get_model_file(biomarker)
model.add_model(biomarker, model_file)
fitter = ModelFitter(model)
# Calculate mean and max progress
mean_min = model.get_mean_min_progress()
mean_max = model.get_mean_max_progress()
# Estimate dpis (and dprs) and save data
if not estimate_dprs or len(visits) == 1:
if estimate_dprs and len(visits) == 1:
print log.WARNING, 'Only one visit, cannot estimate DPR (setting to one)'
rids, diagnoses, dpis = estimate_dpis(measurements,
visits,
fitter,
phase=phase)
dprs = np.ones(len(dpis)).tolist()
else:
rids, diagnoses, dpis, dprs = estimate_dpis_dprs(measurements,
visits,
fitter,
phase=phase)
print log.INFO, 'Saving DPI{0} estimations to {1}...'.format(
'\DPR' if estimate_dprs else '', estimates_file)
pickle.dump((rids, diagnoses, dpis, dprs, mean_min, mean_max),
open(estimates_file, 'wb'))
# Reduce to consistent data sets with bl, m12 and m24 samples
if consistent_data or select_training_set or select_test_set:
consistent_method = 'all' if consistent_data else method
consistent_data_handler = DataHandler.get_data_handler(
method=consistent_method)
consistent_measurements = consistent_data_handler.get_measurements_as_dict(
visits=['bl', 'm12', 'm24'],
select_training_set=select_training_set,
select_test_set=select_test_set,
select_complete=True,
no_regression=True)
consistent_rids = []
consistent_diagnoses = []
consistent_dpis = []
consistent_dprs = []
for i, rid in enumerate(rids):
if rid in consistent_measurements:
consistent_rids.append(rid)
consistent_diagnoses.append(diagnoses[i])
consistent_dpis.append(dpis[i])
consistent_dprs.append(dprs[i])
rids = consistent_rids
diagnoses = consistent_diagnoses
dpis = consistent_dpis
dprs = consistent_dprs
print log.RESULT, 'Selected {0} consistent subjects.'.format(len(dpis))
# Return results
return rids, diagnoses, dpis, dprs, mean_min, mean_max
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m',
'--method',
choices=DataHandler.get_method_choices(),
default='all',
help='the method to collect data for')
parser.add_argument('-b',
'--biomarkers',
nargs='+',
default=None,
help='name of the biomarker to be plotted')
parser.add_argument('-p',
'--phase',
default='mciad',
choices=DataHandler.get_phase_choices(),
help='the phase for which the model is to be trained')
parser.add_argument('-e',
'--extrapolator',
type=str,
choices=['lin', 'sqrt', 'exp'],
default='exp',
help='the type of extrapolator')
parser.add_argument('--xlim',
type=float,
nargs=2,
default=None,
help='force certain x limits for plotting')
parser.add_argument('--ylim',
type=float,
nargs=2,
default=None,
help='force certain y limits for plotting')
parser.add_argument('--no_model',
action='store_true',
default=False,
help='do not plot the fitted model')
parser.add_argument('--no_points',
action='store_true',
default=False,
help='do not plot points')
parser.add_argument('--points_alpha',
type=float,
default=0.25,
help='alpha value of the plotted points')
parser.add_argument('--no_densities',
action='store_true',
default=False,
help='do not plot densities')
parser.add_argument('--no_sample_lines',
action='store_true',
default=False,
help='do not plot the sample lines')
parser.add_argument('--only_densities',
action='store_true',
default=False,
help='only plot densities')
parser.add_argument('--no_extrapolation',
action='store_true',
default=False,
help='do not extrapolate the model')
parser.add_argument('--plot_eta',
type=str,
choices=['lambda', 'mu', 'sigma'],
default=None,
help='plot a predictor function')
parser.add_argument('--plot_errors',
action='store_true',
default=False,
help='plot the errors')
parser.add_argument('--plot_synth_model',
action='store_true',
default=False,
help='plot density distributions for synthetic data')
parser.add_argument('--plot_quantile_label',
action='store_true',
default=False,
help='plot labels on the quantile curces')
parser.add_argument(
'--plot_donohue',
action='store_true',
default=False,
help='plot the trajectory estimated with Donohue et al.')
parser.add_argument('--save_plots',
action='store_true',
default=False,
help='save the plots with a default filename')
parser.add_argument('--plot_file',
type=str,
default=None,
help='filename of the output file')
args = parser.parse_args()
data_handler = DataHandler.get_data_handler(method=args.method,
biomarkers=args.biomarkers,
phase=args.phase)
for biomarker in data_handler.get_biomarker_names():
plot_model(args, data_handler, biomarker)
def plot_dpi_estimates(args, dpis, diagnoses, mean_min, mean_max):
print log.INFO, 'Plotting estimates...'
test_dpi_min, test_dpi_max, _ = ModelFitter.get_test_dpi_range(args.phase)
dpi_range = float(test_dpi_max - test_dpi_min)
dpi_factor = float(args.plot_steps) / dpi_range
# Setup plot
fig, ax = plt.subplots(figsize=(6, 2))
biomarkers_str = args.method if args.biomarkers is None else ', '.join(args.biomarkers)
ax.set_title('DP estimation using {0} at {1}'.format(biomarkers_str, ', '.join(args.visits)))
ax.spines['left'].set_position(('outward', 10))
ax.spines['bottom'].set_position(('outward', 10))
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
xticks = np.linspace(0, args.plot_steps, 7)
ax.set_xticks(xticks)
ax.set_xticklabels([int(float(tick) / dpi_factor + test_dpi_min) for tick in xticks])
# Compute matrix
diagnosis_indices = {0.0: 0, 0.25: 1, 0.5: 1, 0.75: 2, 1.0: 3}
matrix = np.zeros((4, args.plot_steps + 1))
for dpi, diag in zip(dpis, diagnoses):
row = diagnosis_indices[diag]
dpi_index = round((dpi - test_dpi_min) * dpi_factor)
matrix[row, dpi_index] += 1.0
# Draw annotations
dpis = np.array(dpis)
diagnoses = np.array(diagnoses)
medians = []
q25 = []
q75 = []
for diag in [0.0, 0.25, 0.75, 1.0]:
row = diagnosis_indices[diag]
matrix[row] /= np.sum(matrix[row])
indices = np.where(diagnoses == diag)
median = np.median(dpis[indices])
medians.append((median - test_dpi_min) * dpi_factor)
q25.append((median - np.percentile(dpis[indices], 25)) * dpi_factor)
q75.append((np.percentile(dpis[indices], 75) - median) * dpi_factor)
if args.plot_lines:
ax.set_ylim(-0.01, 0.36)
sample_cmap = cmx.ScalarMappable(
norm=colors.Normalize(0.0, 1.0),
cmap=plt.get_cmap(pt.progression_cmap))
for diag in [0.0, 0.25, 0.75, 1.0]:
row = diagnosis_indices[diag]
plt.plot(matrix[row], color=sample_cmap.to_rgba(diag))
else:
ax.set_yticks([0, 1, 2, 3])
ax.set_yticklabels(['CN', 'EMCI', 'LMCI', 'AD'])
cmap = plt.get_cmap('jet') if args.plot_cmap_jet else plt.get_cmap('Greys')
bar_color = 'w' if args.plot_cmap_jet else 'r'
plt.errorbar(medians, [0, 1, 2, 3], xerr=[q25, q75], fmt='none',
ecolor=bar_color, elinewidth=2,
capsize=4, capthick=2)
plt.plot(medians, [0, 1, 2, 3], linestyle='', color=bar_color, marker='|', markersize=15, markeredgewidth=2)
plt.imshow(matrix, cmap=cmap, interpolation='nearest')
plt.axvline((mean_min - test_dpi_min) * dpi_factor, color='k', linestyle=':', alpha=0.6)
plt.axvline((mean_max - test_dpi_min) * dpi_factor, color='k', linestyle=':', alpha=0.6)
plt.axvline((0.0 - test_dpi_min) * dpi_factor, color='k', linestyle='-', alpha=0.6)
if args.phase == 'joint':
data_handler = DataHandler.get_data_handler(method=args.method, biomarkers=args.biomarkers, phase=args.phase)
plt.axvline((data_handler.get_model_offset() - test_dpi_min) * dpi_factor, color='k', linestyle='-', alpha=0.6)
# Draw or save the plot
plt.tight_layout()
if args.plot_file is not None:
plt.savefig(args.plot_file, transparent=True)
else:
plt.show()
plt.close(fig)
def get_biomarker_predictions(visits,
predict_biomarker,
method=None,
biomarkers=None,
phase=None,
recompute_estimates=False,
recompute_predictions=False,
estimate_dprs=False,
select_test_set=False,
consistent_data=False,
exclude_cn=False,
use_last_visit=False,
naive_use_diagnosis=False):
# Get prediction file
data_handler = DataHandler.get_data_handler(method=method,
biomarkers=biomarkers,
phase=phase)
predict_biomarker_str = predict_biomarker.replace(' ', '_')
predict_file_trunk = 'predict_{0}_with_dpr_{1}_{2}{3}.p' if estimate_dprs else 'predict_{0}_with_{1}_{2}{3}.p'
if biomarkers is None:
predict_file_basename = predict_file_trunk.format(
predict_biomarker_str, method, '_'.join(visits),
'_last' if use_last_visit else '')
else:
estimate_biomarkers_string = '_'.join(biomarkers).replace(' ', '_')
predict_file_basename = predict_file_trunk.format(
predict_biomarker_str, estimate_biomarkers_string,
'_'.join(visits), '_last' if use_last_visit else '')
prediction_file = os.path.join(data_handler.get_eval_folder(),
predict_file_basename)
# Read if predictions exist, else recompute
if os.path.isfile(prediction_file) and not recompute_predictions:
# Read biomarker predictions from file
print log.INFO, 'Reading {0} predictions from {1}...'.format(
predict_biomarker, prediction_file)
(rids, diagnoses, values_observed, values_naive,
values_model) = pickle.load(open(prediction_file, 'rb'))
else:
predict_visit = get_predicted_visit(visits)
print log.INFO, 'Predicting {0} at {1}...'.format(
predict_biomarker, predict_visit)
# Get mean changes from file
mean_changes_file = os.path.join(data_handler.get_eval_folder(),
'mean_changes.p')
if not os.path.isfile(mean_changes_file):
print log.ERROR, 'Mean changes unknown, run misc/compute_mean_biomarker_changes.py first!'
mean_changes = pickle.load(open(mean_changes_file, 'rb'))
# Get DPI estimates
rids_all, diagnoses_all, dpis, dprs, _, _ = get_progress_estimates(
visits,
method=method,
biomarkers=biomarkers,
phase=phase,
recompute_estimates=recompute_estimates,
estimate_dprs=estimate_dprs,
select_test_set=select_test_set,
consistent_data=consistent_data)
# Collect biomarker data for test
measurements = data_handler.get_measurements_as_dict(
visits=visits + [predict_visit],
biomarkers=[predict_biomarker],
select_test_set=select_test_set,
select_complete=True)
model = ProgressionModel(
predict_biomarker, data_handler.get_model_file(predict_biomarker))
print log.INFO, 'Predicting {0} for {1}'.format(
predict_biomarker, predict_visit)
rids = []
diagnoses = []
values_observed = []
values_model = []
values_naive = []
for rid, diagnosis, dpi, dpr in zip(rids_all, diagnoses_all, dpis,
dprs):
if rid in measurements:
# Get real biomarker value value at next visit
scantime_first_visit = measurements[rid][visits[0]]['scantime']
scantime_next_visit = measurements[rid][predict_visit][
'scantime']
progress_next_visit = ModelFitter.scantime_to_progress(
scantime_next_visit, scantime_first_visit, dpi, dpr)
value_observed = measurements[rid][predict_visit][
predict_biomarker]
values_observed.append(value_observed)
# Predict biomarker value value at next visit
if use_last_visit:
value = measurements[rid][visits[-1]][predict_biomarker]
scantime = measurements[rid][visits[-1]]['scantime']
progress = ModelFitter.scantime_to_progress(
scantime, scantime_first_visit, dpi, dpr)
mean_quantile = model.approximate_quantile(progress, value)
else:
mean_quantile = 0.0
for visit in visits:
value = measurements[rid][visit][predict_biomarker]
scantime = measurements[rid][visit]['scantime']
progress = ModelFitter.scantime_to_progress(
scantime, scantime_first_visit, dpi, dpr)
mean_quantile += model.approximate_quantile(
progress, value)
mean_quantile /= len(visits)
value_model = model.get_value_at_quantile(
progress_next_visit, mean_quantile)
values_model.append(value_model)
# Predict biomarker value naively
if naive_use_diagnosis:
mean_change = mean_changes[predict_biomarker][diagnosis]
else:
mean_change = mean_changes[predict_biomarker][0.66]
if use_last_visit:
x = measurements[rid][visits[-1]]['scantime']
y = measurements[rid][visits[-1]][predict_biomarker]
intercept = -(mean_change * x - y)
else:
x = np.zeros(len(visits))
y = np.zeros(len(visits))
for i, visit in enumerate(visits):
x[i] = measurements[rid][visit]['scantime']
y[i] = measurements[rid][visit][predict_biomarker]
intercept = -np.sum(mean_change * x - y) / len(x)
value_naive = intercept + mean_change * measurements[rid][
predict_visit]['scantime']
values_naive.append(value_naive)
# Plot estimates
plot = True
if plot and diagnosis > 0.0 and dpr > 0.0:
plot_predictions(predict_biomarker, model, visits,
measurements[rid], dpi, dpr, value_model,
value_naive, mean_quantile, mean_change,
intercept, rid)
# Append rid and diagnosis
rids.append(rid)
diagnoses.append(diagnosis)
# Print result
print log.RESULT, '{0} for subject {1}: Observed: {2}, Naive {3}, Model: {4}'.format(
predict_biomarker, rid, value_observed, value_naive,
value_model)
# Save results
print log.INFO, 'Saving {0} predictions to {1}...'.format(
predict_biomarker, prediction_file)
pickle.dump(
(rids, diagnoses, values_observed, values_naive, values_model),
open(prediction_file, 'wb'))
rids = np.array(rids)
diagnoses = np.array(diagnoses)
values_observed = np.array(values_observed)
values_naive = np.array(values_naive)
values_model = np.array(values_model)
# Exclude healthy subjects
if exclude_cn:
indices = np.where(diagnoses > 0.25)
rids = rids[indices]
diagnoses = diagnoses[indices]
values_observed = values_observed[indices]
values_naive = values_naive[indices]
values_model = values_model[indices]
return rids, diagnoses, values_observed, values_naive, values_model
