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 main():
parser = argparse.ArgumentParser()
parser.add_argument('visits', nargs='+', type=str, help='the viscodes to be sampled')
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('--estimate_dprs', action='store_true', help='recompute the dpis estimations')
parser.add_argument('--recompute_estimates', action='store_true', help='recompute the dpis estimations')
parser.add_argument('--consistent_data', action='store_true', help='us only subjects with bl, m12 and m24 visits')
parser.add_argument('--no_plot', action='store_true', help='do not plot the results')
parser.add_argument('--plot_lines', action='store_true', help='plot graphs instead of matrix')
parser.add_argument('--plot_steps', type=int, default=15, help='number of steps for the DPI scale')
parser.add_argument('--plot_file', type=str, default=None, help='filename of the output file')
parser.add_argument('--plot_cmap_jet', action='store_true', help='use the colour map jet')
args = parser.parse_args()
# Get estimates
_, diagnoses, dpis, dprs, mean_min, mean_max = et.get_progress_estimates(
args.visits,
method=args.method,
biomarkers=args.biomarkers,
phase=args.phase,
estimate_dprs=args.estimate_dprs,
recompute_estimates=args.recompute_estimates,
select_test_set=True,
consistent_data=args.consistent_data)
# Plot results
if not args.no_plot:
plot_dpi_estimates(args, dpis, diagnoses, mean_min, mean_max)
if args.estimate_dprs:
plot_dpi_dpr_distribution(args, dpis, dprs, diagnoses)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('visits', nargs='+', type=str, help='the viscodes of the visits that are available')
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('--predict_biomarker', type=str, default='MMSE', help='the biomarker to predict')
parser.add_argument('--recompute_estimates', action='store_true', help='recompute the dpi / dpr estimations')
parser.add_argument('--recompute_predictions', action='store_true', help='recompute the biomarker predictions')
parser.add_argument('--estimate_dprs', action='store_true', help='estimate dpis and dprs')
parser.add_argument('--consistent_data', action='store_true', help='use only subjects with bl, m12 and m24 visits')
parser.add_argument('--exclude_cn', action='store_true', help='exclude healthy subjects from analysis')
parser.add_argument('--use_last_visit', action='store_true', help='use only the last visit for prediction')
parser.add_argument('--naive_use_diagnosis', action='store_true', help='use the specific mean change for the diagnosis')
parser.add_argument('--no_plot', action='store_true', help='do not plot the results')
parser.add_argument('--plot_file', type=str, default=None, help='filename of the output file')
parser.add_argument('--latex_file', type=str, default=None, help='add output to a LaTeX file')
args = parser.parse_args()
_, diagnoses, values_observed, values_naive, values_model = \
et.get_biomarker_predictions(args.visits, args.predict_biomarker,
method=args.method,
biomarkers=args.biomarkers,
phase=args.phase,
recompute_estimates=args.recompute_estimates,
recompute_predictions=args.recompute_predictions,
estimate_dprs=args.estimate_dprs,
select_test_set=True,
consistent_data=args.consistent_data,
exclude_cn=args.exclude_cn,
use_last_visit=args.use_last_visit,
naive_use_diagnosis=args.naive_use_diagnosis)
if not args.no_plot:
plot_biomarker_predictions(args, diagnoses, values_observed, values_model)
analyse_biomarker_predictions(args, diagnoses, values_observed, values_naive, values_model)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('visits', nargs='+', type=str, help='the viscodes to be sampled')
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('-c', '--classifier', default='svm', choices=['lda', 'svm', 'lsvm', 'rf'], help='the approach used to classify the subjects')
parser.add_argument('--estimate_dprs', action='store_true', help='recompute the dpis estimations')
parser.add_argument('--recompute_estimates', action='store_true', help='recompute the dpis estimations')
parser.add_argument('--consistent_data', action='store_true', help='us only subjects with bl, m12 and m24 visits')
parser.add_argument('--num_folds', type=int, default=10, help='number of folds for the n-fold cross validation')
parser.add_argument('--latex_file', type=str, default=None, help='add output to a LaTeX file')
args = parser.parse_args()
# Get estimates
rids, diagnoses, dpis, dprs, _, _ = et.get_progress_estimates(
args.visits,
method=args.method,
biomarkers=args.biomarkers,
phase=args.phase,
estimate_dprs=args.estimate_dprs,
recompute_estimates=args.recompute_estimates,
consistent_data=args.consistent_data)
# Select converters and non-converters sets
rcds, non_rcds = get_rcds(args, rids, diagnoses, dpis, dprs)
rfds, non_rfds = get_rfds(args, rids, diagnoses, dpis, dprs)
# Analyse output
analyse_decline(args, rids, dpis, dprs, rcds, non_rcds)
analyse_decline(args, rids, dpis, dprs, rfds, non_rfds)
def main():
parser = argparse.ArgumentParser()
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('--predict_biomarker',
type=str,
default='MMSE',
help='the biomarker to predict')
parser.add_argument('--recompute_estimates',
action='store_true',
help='recompute the dpi / dpr estimations')
parser.add_argument('--recompute_predictions',
action='store_true',
help='recompute the biomarker predictions')
parser.add_argument('--plot_file',
type=str,
default=None,
help='filename of the output file')
args = parser.parse_args()
visits = ['bl', 'm12', 'm24']
methods = ['cog', 'vol', 'ml', 'img', 'all']
values = {}
for method in methods:
values.update({method: {}})
_, _, values_observed, values_naive, values_model = \
et.get_biomarker_predictions(visits, args.predict_biomarker,
method=method,
phase=args.phase,
recompute_estimates=args.recompute_estimates,
recompute_predictions=args.recompute_predictions,
estimate_dprs=False,
exclude_cn=True,
select_test_set=True,
consistent_data=True)
values[method].update({'observed': values_observed})
values[method].update({'naive': values_naive})
values[method].update({'model_dpi': values_model})
_, _, values_observed, values_naive, values_model = \
et.get_biomarker_predictions(visits, args.predict_biomarker,
method=method,
phase=args.phase,
recompute_estimates=args.recompute_estimates,
recompute_predictions=args.recompute_predictions,
estimate_dprs=True,
exclude_cn=True,
select_test_set=True,
consistent_data=True)
values[method].update({'model_dpi_dpr': values_model})
plot_errors(args, values, methods)
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 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():
parser = argparse.ArgumentParser()
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('--predict_biomarker', type=str, default='MMSE', help='the biomarker to predict')
parser.add_argument('--recompute_estimates', action='store_true', help='recompute the dpi / dpr estimations')
parser.add_argument('--recompute_predictions', action='store_true', help='recompute the biomarker predictions')
parser.add_argument('--plot_file', type=str, default=None, help='filename of the output file')
args = parser.parse_args()
visits = ['bl', 'm12', 'm24']
methods = ['cog', 'vol', 'ml', 'img', 'all']
values = {}
for method in methods:
values.update({method: {}})
_, _, values_observed, values_naive, values_model = \
et.get_biomarker_predictions(visits, args.predict_biomarker,
method=method,
phase=args.phase,
recompute_estimates=args.recompute_estimates,
recompute_predictions=args.recompute_predictions,
estimate_dprs=False,
exclude_cn=True,
select_test_set=True,
consistent_data=True)
values[method].update({'observed': values_observed})
values[method].update({'naive': values_naive})
values[method].update({'model_dpi': values_model})
_, _, values_observed, values_naive, values_model = \
et.get_biomarker_predictions(visits, args.predict_biomarker,
method=method,
phase=args.phase,
recompute_estimates=args.recompute_estimates,
recompute_predictions=args.recompute_predictions,
estimate_dprs=True,
exclude_cn=True,
select_test_set=True,
consistent_data=True)
values[method].update({'model_dpi_dpr': values_model})
plot_errors(args, values, methods)
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():
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 main():
parser = argparse.ArgumentParser()
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('--consistent_data', action='store_true', help='us only subjects with bl, m12 and m24 visits')
parser.add_argument('--estimate_dprs', action='store_true', help='estimate dpis and dprs')
parser.add_argument('--recompute_estimates', action='store_true', help='recompute the dpi / dpr estimations')
parser.add_argument('--recompute_predictions', action='store_true', help='recompute the biomarker predictions')
parser.add_argument('--exclude_cn', action='store_true', help='exclude healthy subjects from analysis')
args = parser.parse_args()
estimates = {}
methods = ['cog', 'vol', 'ml', 'img', 'all']
for method in methods:
estimates.update({method: {}})
for visits in [['bl'], ['m12'], ['m24'], ['bl', 'm12'], ['m12', 'm24']]:
_, diagnoses, dpis, _, _, _ = et.get_progress_estimates(visits,
method=method,
phase=args.phase,
estimate_dprs=args.estimate_dprs)
diagnoses = np.array(diagnoses)
dpis = np.array(dpis)
visits_string = '_'.join(visits)
estimates[method].update({visits_string: {}})
estimates[method][visits_string].update({'CN': np.mean(dpis[np.where(diagnoses == 0.0)])})
estimates[method][visits_string].update({'EMCI': np.mean(dpis[np.where(diagnoses == 0.25)])})
estimates[method][visits_string].update({'LMCI': np.mean(dpis[np.where(diagnoses == 0.75)])})
estimates[method][visits_string].update({'AD': np.mean(dpis[np.where(diagnoses == 1.0)])})
for method in methods:
print log.INFO, 'Results for {0}'.format(method)
for diagnosis in ['CN', 'EMCI', 'LMCI', 'AD']:
print log.RESULT, '{0: <4}: {1:.2f} {2:.2f} | {3:.2f} '.format(
diagnosis,
estimates[method]['m12'][diagnosis] - estimates[method]['bl'][diagnosis],
estimates[method]['m24'][diagnosis] - estimates[method]['m12'][diagnosis],
estimates[method]['m12_m24'][diagnosis] - estimates[method]['bl_m12'][diagnosis])
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 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 main():
parser = argparse.ArgumentParser()
parser.add_argument('visits',
nargs='+',
type=str,
help='the viscodes to be sampled')
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('--estimate_dprs',
action='store_true',
help='recompute the dpis estimations')
parser.add_argument('--recompute_estimates',
action='store_true',
help='recompute the dpis estimations')
parser.add_argument('--consistent_data',
action='store_true',
help='us only subjects with bl, m12 and m24 visits')
parser.add_argument('--no_plot',
action='store_true',
help='do not plot the results')
parser.add_argument('--plot_lines',
action='store_true',
help='plot graphs instead of matrix')
parser.add_argument('--plot_steps',
type=int,
default=15,
help='number of steps for the DPI scale')
parser.add_argument('--plot_file',
type=str,
default=None,
help='filename of the output file')
parser.add_argument('--plot_cmap_jet',
action='store_true',
help='use the colour map jet')
args = parser.parse_args()
# Get estimates
_, diagnoses, dpis, dprs, mean_min, mean_max = et.get_progress_estimates(
args.visits,
method=args.method,
biomarkers=args.biomarkers,
phase=args.phase,
estimate_dprs=args.estimate_dprs,
recompute_estimates=args.recompute_estimates,
select_test_set=True,
consistent_data=args.consistent_data)
# Plot results
if not args.no_plot:
plot_dpi_estimates(args, dpis, diagnoses, mean_min, mean_max)
if args.estimate_dprs:
plot_dpi_dpr_distribution(args, dpis, dprs, diagnoses)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('visits',
nargs='+',
type=str,
help='the viscodes of the visits that are available')
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('--predict_biomarker',
type=str,
default='MMSE',
help='the biomarker to predict')
parser.add_argument('--recompute_estimates',
action='store_true',
help='recompute the dpi / dpr estimations')
parser.add_argument('--recompute_predictions',
action='store_true',
help='recompute the biomarker predictions')
parser.add_argument('--estimate_dprs',
action='store_true',
help='estimate dpis and dprs')
parser.add_argument('--consistent_data',
action='store_true',
help='use only subjects with bl, m12 and m24 visits')
parser.add_argument('--exclude_cn',
action='store_true',
help='exclude healthy subjects from analysis')
parser.add_argument('--use_last_visit',
action='store_true',
help='use only the last visit for prediction')
parser.add_argument('--naive_use_diagnosis',
action='store_true',
help='use the specific mean change for the diagnosis')
parser.add_argument('--no_plot',
action='store_true',
help='do not plot the results')
parser.add_argument('--plot_file',
type=str,
default=None,
help='filename of the output file')
parser.add_argument('--latex_file',
type=str,
default=None,
help='add output to a LaTeX file')
args = parser.parse_args()
_, diagnoses, values_observed, values_naive, values_model = \
et.get_biomarker_predictions(args.visits, args.predict_biomarker,
method=args.method,
biomarkers=args.biomarkers,
phase=args.phase,
recompute_estimates=args.recompute_estimates,
recompute_predictions=args.recompute_predictions,
estimate_dprs=args.estimate_dprs,
select_test_set=True,
consistent_data=args.consistent_data,
exclude_cn=args.exclude_cn,
use_last_visit=args.use_last_visit,
naive_use_diagnosis=args.naive_use_diagnosis)
if not args.no_plot:
plot_biomarker_predictions(args, diagnoses, values_observed,
values_model)
analyse_biomarker_predictions(args, diagnoses, values_observed,
values_naive, values_model)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('visits',
nargs='+',
type=str,
help='the viscodes to be sampled')
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('-c',
'--classifier',
default='svm',
choices=['lda', 'svm', 'lsvm', 'rf'],
help='the approach used to classify the subjects')
parser.add_argument('--estimate_dprs',
action='store_true',
help='recompute the dpis estimations')
parser.add_argument('--recompute_estimates',
action='store_true',
help='recompute the dpis estimations')
parser.add_argument('--consistent_data',
action='store_true',
help='us only subjects with bl, m12 and m24 visits')
parser.add_argument('--num_folds',
type=int,
default=10,
help='number of folds for the n-fold cross validation')
parser.add_argument(
'--num_runs',
type=int,
default=1,
help='number of runs the x-fold cross-validation is performed')
parser.add_argument('--latex_file',
type=str,
default=None,
help='add output to a LaTeX file')
args = parser.parse_args()
# Get estimates
_, diagnoses, dpis, dprs, _, _ = et.get_progress_estimates(
args.visits,
method=args.method,
biomarkers=args.biomarkers,
phase=args.phase,
estimate_dprs=args.estimate_dprs,
recompute_estimates=args.recompute_estimates,
consistent_data=args.consistent_data)
# Analyse estimates
classify_diagnoses(args, dpis, dprs, diagnoses)
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 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)
