def get_model_differences(args, data_handler, biomarker, offsets):
print log.INFO, 'Comparing models for {0}...'.format(biomarker)
model_file = data_handler.get_model_file(biomarker)
print model_file
if not os.path.isfile(model_file):
print log.ERROR, 'Model file not found: {0}'.format(model_file)
return
donohue_model_file = os.path.join(
data_handler._conf.models_folder, 'denohue',
'population_{0}.csv'.format(biomarker.replace(' ', '.')))
if not os.path.isfile(donohue_model_file):
print log.ERROR, 'Donohue Model file not found: {0}'.format(model_file)
return
# Read Donohue model
r = mlab.csv2rec(donohue_model_file)
progrs = r[r.dtype.names[0]] * 30.44
progrs = progrs[::100]
vals_donohue = r[r.dtype.names[1]]
vals_donohue = vals_donohue[::100]
# Read my model
pm = ProgressionModel(biomarker,
model_file,
extrapolator=args.extrapolator)
diffs = np.empty(len(offsets))
for i, offset in enumerate(offsets):
vals_mine = pm.get_quantile_curve(progrs + offset, 0.5)
normalizer = max(np.max(vals_mine), np.max(vals_donohue))
diffs[i] = np.mean(np.abs(vals_donohue - vals_mine)) / normalizer
return diffs
def get_model_differences(args, data_handler, biomarker, offsets):
print log.INFO, 'Comparing models for {0}...'.format(biomarker)
model_file = data_handler.get_model_file(biomarker)
print model_file
if not os.path.isfile(model_file):
print log.ERROR, 'Model file not found: {0}'.format(model_file)
return
donohue_model_file = os.path.join(data_handler._conf.models_folder,
'denohue', 'population_{0}.csv'.format(biomarker.replace(' ', '.')))
if not os.path.isfile(donohue_model_file):
print log.ERROR, 'Donohue Model file not found: {0}'.format(model_file)
return
# Read Donohue model
r = mlab.csv2rec(donohue_model_file)
progrs = r[r.dtype.names[0]] * 30.44
progrs = progrs[::100]
vals_donohue = r[r.dtype.names[1]]
vals_donohue = vals_donohue[::100]
# Read my model
pm = ProgressionModel(biomarker, model_file, extrapolator=args.extrapolator)
diffs = np.empty(len(offsets))
for i, offset in enumerate(offsets):
vals_mine = pm.get_quantile_curve(progrs + offset, 0.5)
normalizer = max(np.max(vals_mine), np.max(vals_donohue))
diffs[i] = np.mean(np.abs(vals_donohue - vals_mine)) /normalizer
return diffs
def evaluate_synth_model(model_file,
biomarker,
progress_linspace,
number_of_value_steps,
metric='area'):
# Define progress steps
pm = ProgressionModel(biomarker, model_file)
progresses = np.linspace(progress_linspace[0], progress_linspace[1],
progress_linspace[2])
# Define value steps
min_val = float('inf')
max_val = float('-inf')
for quantile in [0.01, 0.99]:
curve = pm.get_quantile_curve(progresses, quantile)
min_val = min(min_val, np.min(curve))
max_val = max(max_val, np.max(curve))
values = np.linspace(min_val, max_val, number_of_value_steps)
# Get mean error
error = 0
if metric == 'area':
for progr in progresses:
probs_model = [
SynthModel.get_probability(biomarker, progr, v) for v in values
]
probs_fit = pm.get_density_distribution(values, progr)
error += np.sum(
np.abs(np.array(probs_fit) - np.array(probs_model)))
error *= (values[1] - values[0]) / len(progresses)
elif metric == 'peakdist':
for progr in progresses:
probs_model = [
SynthModel.get_probability(biomarker, progr, v) for v in values
]
probs_fit = pm.get_density_distribution(values, progr)
peak_model = values[np.argsort(probs_model)[-1]]
peak_fit = values[np.argsort(probs_fit)[-1]]
error += np.abs(peak_fit - peak_model)
error /= len(progresses)
elif metric == 'maxdist':
for value in values:
probs_model = [
SynthModel.get_probability(biomarker, p, value)
for p in progresses
]
probs_fit = [
pm.get_density_distribution([value], p) for p in progresses
]
max_model = progresses[np.argsort(probs_model)[-1]]
max_fit = progresses[np.argsort(probs_fit)[-1]]
error += np.abs(max_fit - max_model)
error /= len(values)
else:
print log.ERROR, 'Metric unknown: {0}'.format(metric)
return error
def evaluate_synth_model(model_file, biomarker, progress_linspace, number_of_value_steps, metric='area'):
# Define progress steps
pm = ProgressionModel(biomarker, model_file)
progresses = np.linspace(progress_linspace[0],
progress_linspace[1],
progress_linspace[2])
# Define value steps
min_val = float('inf')
max_val = float('-inf')
for quantile in [0.01, 0.99]:
curve = pm.get_quantile_curve(progresses, quantile)
min_val = min(min_val, np.min(curve))
max_val = max(max_val, np.max(curve))
values = np.linspace(min_val, max_val, number_of_value_steps)
# Get mean error
error = 0
if metric == 'area':
for progr in progresses:
probs_model = [SynthModel.get_probability(biomarker, progr, v) for v in values]
probs_fit = pm.get_density_distribution(values, progr)
error += np.sum(np.abs(np.array(probs_fit) - np.array(probs_model)))
error *= (values[1] - values[0]) / len(progresses)
elif metric == 'peakdist':
for progr in progresses:
probs_model = [SynthModel.get_probability(biomarker, progr, v) for v in values]
probs_fit = pm.get_density_distribution(values, progr)
peak_model = values[np.argsort(probs_model)[-1]]
peak_fit = values[np.argsort(probs_fit)[-1]]
error += np.abs(peak_fit - peak_model)
error /= len(progresses)
elif metric == 'maxdist':
for value in values:
probs_model = [SynthModel.get_probability(biomarker, p, value) for p in progresses]
probs_fit = [pm.get_density_distribution([value], p) for p in progresses]
max_model = progresses[np.argsort(probs_model)[-1]]
max_fit = progresses[np.argsort(probs_fit)[-1]]
error += np.abs(max_fit - max_model)
error /= len(values)
else:
print log.ERROR, 'Metric unknown: {0}'.format(metric)
return error
def evaluate_biomarker_cover(args, data_handler, biomarker):
model_file = data_handler.get_model_file(biomarker)
eval_file = model_file.replace('.csv', '_eval_{0}.csv'.format(args.metric))
if os.path.isfile(eval_file) and not args.recompute_metric:
print log.SKIP, 'Evaluation file already existing: {0}'.format(eval_file)
elif not os.path.isfile(model_file):
print log.ERROR, 'Model file not found: {0}!'.format(model_file)
else:
model = ProgressionModel(biomarker, model_file)
# Determine value and progress interval
progresses = np.linspace(model.min_progress, model.max_progress, args.progress_samples)
median_curve = model.get_quantile_curve(progresses, 0.5)
min_value = np.min(median_curve)
max_value = np.max(median_curve)
print log.INFO, 'Evaluating {0} steps in progress interval [{1}, {2}] for values in [{3}, {4}]...'.format(
args.progress_samples, progresses[0], progresses[-1], min_value, max_value)
# Compute error
writer = csv.writer(open(eval_file, 'wb'), delimiter=',')
writer.writerow(['progress', 'error'])
# Compute error
total_error = 0
for progress in progresses:
min_q = model.approximate_quantile(progress, min_value)
max_q = model.approximate_quantile(progress, max_value)
quantile_range = max_q - min_q
total_error += quantile_range
writer.writerow([progress, quantile_range])
total_error /= len(progresses)
print log.RESULT, 'Total error {0}: {1}'.format(biomarker, total_error)
def plot_biomarker(data_handler, biomarker, measurements, dpi, dpr):
"""
Plot the model of one biomarker with the fitted values
:param data_handler: the data handler
:param biomarker: the biomarker to plot
:param measurements: the measurements containing the biomarker samples of one subject
:param dpi: the estimated DPI
:param dpr: the estimated DPR
"""
model_file = data_handler.get_model_file(biomarker)
if not os.path.isfile(model_file):
print log.ERROR, 'Model file not found: {0}'.format(model_file)
return
print log.INFO, 'Generating plot for {0}...'.format(biomarker)
#
# Read model
#
pm = ProgressionModel(biomarker, model_file)
progress_extrapolate = 0.3 * (pm.max_progress - pm.min_progress)
min_progress_extrapolate = int(pm.min_progress - progress_extrapolate)
max_progress_extrapolate = int(pm.max_progress + progress_extrapolate)
progress_linspace_ex1 = np.linspace(min_progress_extrapolate,
pm.min_progress, 20)
progress_linspace_int = np.linspace(pm.min_progress, pm.max_progress, 60)
progress_linspace_ex2 = np.linspace(pm.max_progress,
max_progress_extrapolate, 20)
#
# Setup plot
#
biomarker_string = pt.get_biomarker_string(biomarker)
figure_width = 6
fig = plt.figure(figsize=(figure_width, 5))
ax1 = plt.subplot(1, 1, 1)
pt.setup_axes(plt, ax1, xgrid=False, ygrid=False)
ax1.set_title(
'Model for {0} with fitted sample values'.format(biomarker_string))
ax1.set_xlabel('Disease progress (days before/after conversion to MCI)')
ax1.set_ylabel(DataHandler.get_biomarker_unit(biomarker))
ax1.set_xlim(min_progress_extrapolate, max_progress_extrapolate)
#
# Plot the percentile curves of the fitted model
#
ax1.axvline(pm.min_progress, color='0.15', linestyle=':')
ax1.axvline(pm.max_progress, color='0.15', linestyle=':')
quantiles = [0.1, 0.25, 0.5, 0.75, 0.9]
grey_values = ['0.4', '0.2', '0', '0.2', '0.4']
for grey_value, quantile in zip(grey_values, quantiles):
curve_int = pm.get_quantile_curve(progress_linspace_int, quantile)
ax1.plot(progress_linspace_int, curve_int, color=grey_value)
curve_ex1 = pm.get_quantile_curve(progress_linspace_ex1, quantile)
curve_ex2 = pm.get_quantile_curve(progress_linspace_ex2, quantile)
ax1.plot(progress_linspace_ex1, curve_ex1, '--', color=grey_value)
ax1.plot(progress_linspace_ex2, curve_ex2, '--', color=grey_value)
label = 'q = {0}'.format(quantile * 100)
ax1.text(progress_linspace_int[-1] + 100,
curve_int[-1],
label,
fontsize=10)
#
# Plot points
#
progr_points = []
value_points = []
diagn_points = []
for visit in measurements[0]:
if biomarker in measurements[0][visit]:
progress = measurements[0][visit]['scantime'] * dpr + dpi
value = measurements[0][visit][biomarker]
progr_points.append(progress)
value_points.append(value)
diagn_points.append(1.0)
ax1.axvline(progress, color='b', linestyle='--')
ax1.text(progress + 150, value, visit, color='b', fontsize=10)
ax1.scatter(progr_points,
value_points,
s=25.0,
color='b',
edgecolor='none',
vmin=0.0,
vmax=1.0,
alpha=0.9)
#
# Draw or save the plot
#
plt.tight_layout()
plt.show()
plt.close(fig)
def plot_biomarker(data_handler, biomarker, measurements, dpi, dpr):
"""
Plot the model of one biomarker with the fitted values
:param data_handler: the data handler
:param biomarker: the biomarker to plot
:param measurements: the measurements containing the biomarker samples of one subject
:param dpi: the estimated DPI
:param dpr: the estimated DPR
"""
model_file = data_handler.get_model_file(biomarker)
if not os.path.isfile(model_file):
print log.ERROR, 'Model file not found: {0}'.format(model_file)
return
print log.INFO, 'Generating plot for {0}...'.format(biomarker)
#
# Read model
#
pm = ProgressionModel(biomarker, model_file)
progress_extrapolate = 0.3 * (pm.max_progress - pm.min_progress)
min_progress_extrapolate = int(pm.min_progress - progress_extrapolate)
max_progress_extrapolate = int(pm.max_progress + progress_extrapolate)
progress_linspace_ex1 = np.linspace(min_progress_extrapolate, pm.min_progress, 20)
progress_linspace_int = np.linspace(pm.min_progress, pm.max_progress, 60)
progress_linspace_ex2 = np.linspace(pm.max_progress, max_progress_extrapolate, 20)
#
# Setup plot
#
biomarker_string = pt.get_biomarker_string(biomarker)
figure_width = 6
fig = plt.figure(figsize=(figure_width, 5))
ax1 = plt.subplot(1, 1, 1)
pt.setup_axes(plt, ax1, xgrid=False, ygrid=False)
ax1.set_title('Model for {0} with fitted sample values'.format(biomarker_string))
ax1.set_xlabel('Disease progress (days before/after conversion to MCI)')
ax1.set_ylabel(DataHandler.get_biomarker_unit(biomarker))
ax1.set_xlim(min_progress_extrapolate, max_progress_extrapolate)
#
# Plot the percentile curves of the fitted model
#
ax1.axvline(pm.min_progress, color='0.15', linestyle=':')
ax1.axvline(pm.max_progress, color='0.15', linestyle=':')
quantiles = [0.1, 0.25, 0.5, 0.75, 0.9]
grey_values = ['0.4', '0.2', '0', '0.2', '0.4']
for grey_value, quantile in zip(grey_values, quantiles):
curve_int = pm.get_quantile_curve(progress_linspace_int, quantile)
ax1.plot(progress_linspace_int, curve_int, color=grey_value)
curve_ex1 = pm.get_quantile_curve(progress_linspace_ex1, quantile)
curve_ex2 = pm.get_quantile_curve(progress_linspace_ex2, quantile)
ax1.plot(progress_linspace_ex1, curve_ex1, '--', color=grey_value)
ax1.plot(progress_linspace_ex2, curve_ex2, '--', color=grey_value)
label = 'q = {0}'.format(quantile * 100)
ax1.text(progress_linspace_int[-1] + 100, curve_int[-1], label, fontsize=10)
#
# Plot points
#
progr_points = []
value_points = []
diagn_points = []
for visit in measurements[0]:
if biomarker in measurements[0][visit]:
progress = measurements[0][visit]['scantime'] * dpr + dpi
value = measurements[0][visit][biomarker]
progr_points.append(progress)
value_points.append(value)
diagn_points.append(1.0)
ax1.axvline(progress, color='b', linestyle='--')
ax1.text(progress + 150, value, visit, color='b', fontsize=10)
ax1.scatter(progr_points, value_points, s=25.0, color='b', edgecolor='none',
vmin=0.0, vmax=1.0, alpha=0.9)
#
# Draw or save the plot
#
plt.tight_layout()
plt.show()
plt.close(fig)
def plot_model(args, data_handler, biomarker):
model_file = data_handler.get_model_file(biomarker)
if not os.path.isfile(model_file):
print log.ERROR, 'Model file not found: {0}'.format(model_file)
return
print log.INFO, 'Generating plot for {0}...'.format(biomarker)
plot_synth_model = args.plot_synth_model and biomarker in SynthModel.get_biomarker_names(
)
#
# Read model
#
pm = ProgressionModel(biomarker,
model_file,
extrapolator=args.extrapolator)
progress_extrapolate = 0.3 * (pm.max_progress - pm.min_progress)
min_progress_extrapolate = int(pm.min_progress - progress_extrapolate)
max_progress_extrapolate = int(pm.max_progress + progress_extrapolate)
progress_linspace_ex1 = np.linspace(min_progress_extrapolate,
pm.min_progress, 20)
progress_linspace_int = np.linspace(pm.min_progress, pm.max_progress, 60)
progress_linspace_ex2 = np.linspace(pm.max_progress,
max_progress_extrapolate, 20)
# Calc min and max val in interval between 1% and 99% percentie
min_val, max_val = pm.get_value_range([0.1, 0.9])
# progress_linspace = np.linspace(min_progress_extrapolate, max_progress_extrapolate, 100)
# min_val = float('inf')
# max_val = float('-inf')
# for quantile in [0.1, 0.9]:
# curve = pm.get_quantile_curve(progress_linspace, quantile)
# min_val = min(min_val, np.min(curve))
# max_val = max(max_val, np.max(curve))
#
# Setup plot
#
biomarker_string = pt.get_biomarker_string(biomarker)
figure_width = 6 if args.no_densities or args.only_densities else 12
fig = plt.figure(figsize=(figure_width, 5))
if args.only_densities:
ax1 = None
ax2 = plt.subplot(1, 1, 1)
pt.setup_axes(plt, ax2, xgrid=False, ygrid=False)
elif args.no_densities:
ax1 = plt.subplot(1, 1, 1)
ax2 = None
pt.setup_axes(plt, ax1, xgrid=False, ygrid=False)
else:
ax1 = plt.subplot(1, 2, 1)
ax2 = plt.subplot(1, 2, 2)
pt.setup_axes(plt, ax1, xgrid=False, ygrid=False)
pt.setup_axes(plt, ax2)
if not args.only_densities:
if args.no_model and not args.plot_synth_model:
ax1.set_title('Aligned samples for {0}'.format(biomarker_string))
else:
ax1.set_title('Quantile curves for {0}'.format(biomarker_string))
if args.phase == 'mciad':
ax1.set_xlabel(
'Disease progress (days before/after conversion to AD)')
else:
ax1.set_xlabel(
'Disease progress (days before/after conversion to MCI)')
ax1.set_ylabel(DataHandler.get_biomarker_unit(biomarker))
if args.xlim is not None:
ax1.set_xlim(args.xlim[0], args.xlim[1])
else:
ax1.set_xlim(min_progress_extrapolate, max_progress_extrapolate)
if args.ylim is not None:
ax1.set_ylim(args.ylim[0], args.ylim[1])
#
# Plot the percentile curves of the fitted model
#
if not args.no_model and not args.only_densities:
ax1.axvline(pm.min_progress, color='0.15', linestyle=':')
ax1.axvline(pm.max_progress, color='0.15', linestyle=':')
quantiles = [0.1, 0.25, 0.5, 0.75, 0.9]
grey_values = ['0.4', '0.2', '0', '0.2', '0.4']
for grey_value, quantile in zip(grey_values, quantiles):
curve_int = pm.get_quantile_curve(progress_linspace_int, quantile)
ax1.plot(progress_linspace_int, curve_int, color=grey_value)
if not args.no_extrapolation:
curve_ex1 = pm.get_quantile_curve(progress_linspace_ex1,
quantile)
curve_ex2 = pm.get_quantile_curve(progress_linspace_ex2,
quantile)
ax1.plot(progress_linspace_ex1,
curve_ex1,
'--',
color=grey_value)
ax1.plot(progress_linspace_ex2,
curve_ex2,
'--',
color=grey_value)
if args.plot_quantile_label:
label = '$q={0}\%$'.format(quantile * 100)
ax1.text(progress_linspace_int[-1] + 10,
curve_int[-1],
label,
fontsize=10)
if args.plot_donohue:
print 'Plotting Donohue'
donohue_file = os.path.join(
data_handler._conf.models_folder, 'donohue',
'population_{0}.csv'.format(biomarker.replace(' ', '.')))
if not os.path.isfile(donohue_file):
print log.ERROR, 'Donohue model file not found: {0}'.format(
donohue_file)
return
r = mlab.csv2rec(donohue_file)
if args.method == 'joint':
offset = 2200
else:
offset = 300
progrs = r[r.dtype.names[0]] * 30.44 + offset
vals = r[r.dtype.names[1]]
curve_donohue = []
progr_donohue = []
for p in progress_linspace_int:
if progrs[0] < p < progrs[-1]:
i = 1
while p > progrs[i]:
i += 1
# TODO linear interpolation
progr_donohue.append(progrs[i])
curve_donohue.append(vals[i])
ax1.plot(progr_donohue,
curve_donohue,
'--',
color='b',
linewidth=2)
#
# Plot synthetic model curve
#
if plot_synth_model:
progress_linspace_synth = np.linspace(-2500, 2500, 100)
quantiles = [0.1, 0.25, 0.5, 0.75, 0.9]
alphas = [0.4, 0.7, 1.0, 0.7, 0.4]
for quantile, alpha in zip(quantiles, alphas):
curve_synth = [
SynthModel.get_distributed_value(biomarker, p, cdf=quantile)
for p in progress_linspace_synth
]
ax1.plot(progress_linspace_synth,
curve_synth,
color='b',
alpha=alpha)
#
# Plot predictor function
#
if args.plot_eta is not None and not args.only_densities:
# Get second axis of plot 1
ax1b = ax1.twinx()
# Plot all progresses
# ax1b.scatter(pm.all_progresses, pm.all_mus, facecolor='b', marker='o', edgecolor='none', alpha=0.2)
ax1b.text(pm.progresses[-1],
pm.sigmas[-1],
'$\mu$',
color='b',
fontsize=11)
# Plot binned progresses
ax1b.scatter(pm.progresses, pm.sigmas, color='b', marker='x')
# Plot interpolated model
mus = [pm.get_eta(pm.sigmas, p) for p in progress_linspace_int]
ax1b.plot(progress_linspace_int, mus, color='b')
if not args.no_extrapolation:
mus = [pm.get_eta(pm.sigmas, p) for p in progress_linspace_ex1]
ax1b.plot(progress_linspace_ex1, mus, '--', color='b')
mus = [pm.get_eta(pm.sigmas, p) for p in progress_linspace_ex2]
ax1b.plot(progress_linspace_ex2, mus, '--', color='b')
if args.xlim is not None:
ax1b.set_xlim(args.xlim[0], args.xlim[1])
else:
ax1b.set_xlim(min_progress_extrapolate, max_progress_extrapolate)
#
# Plot errors
#
if args.plot_errors and not args.only_densities:
eval_file = model_file.replace('.csv', '_eval_cover.csv')
if not os.path.isfile(eval_file):
print log.ERROR, 'Evaluation file not found: {0}'.format(eval_file)
else:
m = mlab.csv2rec(eval_file)
progresses = m['progress']
errors = m['error']
# Get second axis of plot 1
ax1b = ax1.twinx()
# ax1b.set_ylim(0, max(150, 1.2 * np.max(errors)))
ax1b.plot(progresses, errors, color='g', marker='x')
ax1b.text(progresses[-1],
errors[-1],
'Discr.',
color='g',
fontsize=11)
ax1b.axhline(np.mean(errors), color='g', linestyle='--', alpha=0.5)
median_curve = pm.get_quantile_curve(progresses, 0.5)
min_value = np.min(median_curve)
max_value = np.max(median_curve)
rect = mpl.patches.Rectangle((progresses[0], min_value),
progresses[-1] - progresses[0],
max_value - min_value,
fc=(0.0, 0.5, 0.0, 0.1),
ec=(0.0, 0.5, 0.0, 0.8),
linewidth=1)
ax1.add_patch(rect)
#
# Plot points
#
if not args.no_points and not args.only_densities:
samples_file = data_handler.get_samples_file(biomarker)
if not os.path.isfile(samples_file):
print log.ERROR, 'Samples file not found: {0}'.format(samples_file)
else:
m = mlab.csv2rec(samples_file)
progr_points = m['progress']
value_points = m['value']
# diagn_points = [0.5 if p < 0 else 1.0 for p in progr_points]
diagn_points = m['diagnosis']
diagn_points[(0.25 <= diagn_points) & (diagn_points <= 0.75)] = 0.5
print log.INFO, 'Plotting {0} sample points...'.format(
len(progr_points))
ax1.scatter(progr_points,
value_points,
s=15.0,
c=diagn_points,
edgecolor='none',
vmin=0.0,
vmax=1.0,
cmap=pt.progression_cmap,
alpha=args.points_alpha)
if args.phase == 'cnmci':
rects = [
mpl.patches.Rectangle(
(0, 0),
1,
1,
fc=pt.color_cn + (args.points_alpha, ),
linewidth=0),
mpl.patches.Rectangle(
(0, 0),
1,
1,
fc=pt.color_mci + (args.points_alpha, ),
linewidth=0)
]
labels = ['CN', 'MCI']
elif args.phase == 'mciad':
rects = [
mpl.patches.Rectangle(
(0, 0),
1,
1,
fc=pt.color_mci + (args.points_alpha, ),
linewidth=0),
mpl.patches.Rectangle(
(0, 0),
1,
1,
fc=pt.color_ad + (args.points_alpha, ),
linewidth=0)
]
labels = ['MCI', 'AD']
else:
rects = [
mpl.patches.Rectangle(
(0, 0),
1,
1,
fc=pt.color_cn + (args.points_alpha, ),
linewidth=0),
mpl.patches.Rectangle(
(0, 0),
1,
1,
fc=pt.color_mci + (args.points_alpha, ),
linewidth=0),
mpl.patches.Rectangle(
(0, 0),
1,
1,
fc=pt.color_ad + (args.points_alpha, ),
linewidth=0)
]
labels = ['CN', 'MCI', 'AD']
legend = ax1.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))
#
# Plot PDFs
#
progr_samples = [-2000, -1000, 0, 1000, 2000, 3000, 4000] if args.phase == 'joint' else \
[-2000, -1500, -1000, -500, 0, 500, 1000, 1500, 2000]
if args.phase == 'cnmci':
vmin = -2000
vmax = 6000
elif args.phase == 'mciad':
vmin = -6000
vmax = 2000
elif args.phase == 'joint':
vmin = -2000
vmax = 4000
sample_cmap = cmx.ScalarMappable(norm=colors.Normalize(vmin=vmin,
vmax=vmax),
cmap=plt.get_cmap(pt.progression_cmap))
if not args.no_sample_lines and not args.only_densities:
for progr in progr_samples:
if not args.no_extrapolation or pm.min_progress < progr < pm.max_progress:
# sample_color = sample_cmap.to_rgba(progr_samples.index(progr))
sample_color = sample_cmap.to_rgba(progr)
linestyle = '--' if progr < pm.min_progress or progr > pm.max_progress else '-'
ax1.axvline(progr,
color=sample_color,
linestyle=linestyle,
alpha=0.3)
if not args.no_densities:
ax2.set_title(
'Probability density function for {0}'.format(biomarker_string))
ax2.set_xlabel(DataHandler.get_biomarker_unit(biomarker))
ax2.set_ylabel('Probability')
if args.ylim is None:
values = np.linspace(min_val, max_val, 250)
ax2.set_xlim(min_val, max_val)
else:
values = np.linspace(args.ylim[0], args.ylim[1], 250)
ax2.set_xlim(args.ylim[0], args.ylim[1])
for progr in progr_samples:
if not args.no_extrapolation or pm.min_progress < progr < pm.max_progress:
# sample_color = sample_cmap.to_rgba(progr_samples.index(progr))
sample_color = sample_cmap.to_rgba(progr)
linestyle = '--' if progr < pm.min_progress or progr > pm.max_progress else '-'
probs = pm.get_density_distribution(values, progr)
ax2.plot(values,
probs,
label=str(progr),
color=sample_color,
linestyle=linestyle)
if plot_synth_model:
probs = [
SynthModel.get_probability(biomarker, progr, v)
for v in values
]
ax2.plot(values, probs, color='b', linestyle='--')
legend = ax2.legend(fontsize=10, loc='best', framealpha=0.9)
legend.get_frame().set_edgecolor((0.6, 0.6, 0.6))
#
# Draw or save the plot
#
plt.tight_layout()
if args.save_plots or args.plot_file is not None:
if args.plot_file is not None:
plot_filename = args.plot_file
else:
plot_filename = model_file.replace('.csv', '.pdf')
plt.savefig(plot_filename, transparent=True)
else:
plt.show()
plt.close(fig)
def evaluate_curves(biomarker_name, num_samples=200, show_plots=False, csig=0):
biomarker = 'synth_{0}'.format(biomarker_name)
print log.INFO, 'Evaluating {0} for {1} samples, csig={2}...'.format(biomarker, num_samples, csig)
donohue_model_path = '/Development/DiseaseProgressionModel/models/donohue/'
vgam_model_path = '/Development/DiseaseProgressionModel/models/synth/'
# Setup plot
if show_plots:
fig = plt.figure()
ax = plt.subplot(1, 1, 1)
pt.setup_axes(plt, ax, xgrid=False, ygrid=False)
ax.set_title('')
ax.set_xlabel('')
else:
fig = None
ax = None
# Initialise values
offset_donohue = 0 # 182.5
errors_donohue = []
errors_vgam_mean = []
errors_vgam_median = []
# Get real curve values
progress_linspace = np.linspace(-1500, 1500)
mean_curve = [SynthModel.get_mean_value(biomarker, p) for p in progress_linspace]
median_curve = [SynthModel.get_distributed_value(biomarker, p, cdf=0.5) for p in progress_linspace]
# Plot synthetic model curve
if show_plots:
progress_linspace_synth = np.linspace(-2500, 2500, 100)
quantiles = [0.1, 0.25, 0.5, 0.75, 0.9]
alphas = [0.4, 0.7, 1.0, 0.7, 0.4]
for quantile, alpha in zip(quantiles, alphas):
curve_synth = [SynthModel.get_distributed_value(biomarker, p, cdf=quantile)
for p in progress_linspace_synth]
ax.plot(progress_linspace_synth, curve_synth, color='b', alpha=alpha)
curve_synth = [SynthModel.get_mean_value(biomarker, p) for p in progress_linspace_synth]
ax.plot(progress_linspace_synth, curve_synth, '--', color='b')
# Get values for mean calculation
end_values = [SynthModel.get_distributed_value(biomarker, progress_linspace[0], cdf=0.001),
SynthModel.get_distributed_value(biomarker, progress_linspace[-1], cdf=0.001),
SynthModel.get_distributed_value(biomarker, progress_linspace[0], cdf=0.999),
SynthModel.get_distributed_value(biomarker, progress_linspace[-1], cdf=0.999)]
values = np.linspace(min(end_values), max(end_values), 100)
for run in range(100):
# Get Donohue model
donohue_file = os.path.join(donohue_model_path,
'population_value-{0}_csig{1}_run{2}.csv'.format(biomarker_name, csig, run))
r = mlab.csv2rec(donohue_file)
progrs = r[r.dtype.names[0]] - offset_donohue
vals = r[r.dtype.names[1]]
curve_donohue = []
progr_donohue = []
for p in progress_linspace:
if progrs[0] < p < progrs[-1]:
i = 1
while p > progrs[i]:
i += 1
progr_donohue.append(float(progrs[i]))
curve_donohue.append(float(vals[i]))
else:
print log.WARNING, 'Model scope too small... skipping!'
continue
# Get VGAM model
if csig == 0:
vgam_model_file = os.path.join(vgam_model_path,
'{0}_model_{1}_longitudinal_{2}.csv'.format(biomarker,
num_samples, run))
else:
vgam_model_file = os.path.join(vgam_model_path,
'{0}_model_{1}_longitudinal_csig{2}.0_{3}.csv'.format(biomarker,
num_samples,
csig, run))
pm = ProgressionModel(biomarker, vgam_model_file)
curve_vgam_median = pm.get_quantile_curve(progress_linspace, 0.5)
curve_vgam_mean = [np.sum(pm.get_density_distribution(values, p) * values /
np.sum(pm.get_density_distribution(values, p))) for p in progress_linspace]
# Calculate errors
errors_donohue.append(np.mean(np.abs(np.array(curve_donohue) - np.array(mean_curve))))
errors_vgam_mean.append(np.mean(np.abs(np.array(curve_vgam_mean) - np.array(mean_curve))))
errors_vgam_median.append(np.mean(np.abs(np.array(curve_vgam_median) - np.array(median_curve))))
if show_plots:
ax.plot(progr_donohue, curve_donohue, '--', color='g', alpha=0.2, linewidth=2)
# ax.plot(progress_linspace, curve_vgam_median, '-', color='r', alpha=0.2, linewidth=2)
ax.plot(progress_linspace, curve_vgam_mean, '--', color='r', alpha=0.2, linewidth=2)
print log.RESULT, 'Donohue (mean):', np.mean(errors_donohue), np.var(errors_donohue)
print log.RESULT, 'VGAM (mean):', np.mean(errors_vgam_mean), np.var(errors_vgam_mean)
print log.RESULT, 'VGAM (median):', np.mean(errors_vgam_median), np.var(errors_vgam_median)
# Draw or save the plot
if show_plots:
plt.tight_layout()
plt.show()
plt.close(fig)
def plot_model(args, data_handler, biomarker):
model_file = data_handler.get_model_file(biomarker)
if not os.path.isfile(model_file):
print log.ERROR, 'Model file not found: {0}'.format(model_file)
return
print log.INFO, 'Generating plot for {0}...'.format(biomarker)
plot_synth_model = args.plot_synth_model and biomarker in SynthModel.get_biomarker_names()
#
# Read model
#
pm = ProgressionModel(biomarker, model_file, extrapolator=args.extrapolator)
progress_extrapolate = 0.3 * (pm.max_progress - pm.min_progress)
min_progress_extrapolate = int(pm.min_progress - progress_extrapolate)
max_progress_extrapolate = int(pm.max_progress + progress_extrapolate)
progress_linspace_ex1 = np.linspace(min_progress_extrapolate, pm.min_progress, 20)
progress_linspace_int = np.linspace(pm.min_progress, pm.max_progress, 60)
progress_linspace_ex2 = np.linspace(pm.max_progress, max_progress_extrapolate, 20)
# Calc min and max val in interval between 1% and 99% percentie
min_val, max_val = pm.get_value_range([0.1, 0.9])
# progress_linspace = np.linspace(min_progress_extrapolate, max_progress_extrapolate, 100)
# min_val = float('inf')
# max_val = float('-inf')
# for quantile in [0.1, 0.9]:
# curve = pm.get_quantile_curve(progress_linspace, quantile)
# min_val = min(min_val, np.min(curve))
# max_val = max(max_val, np.max(curve))
#
# Setup plot
#
biomarker_string = pt.get_biomarker_string(biomarker)
figure_width = 6 if args.no_densities or args.only_densities else 12
fig = plt.figure(figsize=(figure_width, 5))
if args.only_densities:
ax1 = None
ax2 = plt.subplot(1, 1, 1)
pt.setup_axes(plt, ax2, xgrid=False, ygrid=False)
elif args.no_densities:
ax1 = plt.subplot(1, 1, 1)
ax2 = None
pt.setup_axes(plt, ax1, xgrid=False, ygrid=False)
else:
ax1 = plt.subplot(1, 2, 1)
ax2 = plt.subplot(1, 2, 2)
pt.setup_axes(plt, ax1, xgrid=False, ygrid=False)
pt.setup_axes(plt, ax2)
if not args.only_densities:
if args.no_model and not args.plot_synth_model:
ax1.set_title('Aligned samples for {0}'.format(biomarker_string))
else:
ax1.set_title('Quantile curves for {0}'.format(biomarker_string))
if args.phase == 'mciad':
ax1.set_xlabel('Disease progress (days before/after conversion to AD)')
else:
ax1.set_xlabel('Disease progress (days before/after conversion to MCI)')
ax1.set_ylabel(DataHandler.get_biomarker_unit(biomarker))
if args.xlim is not None:
ax1.set_xlim(args.xlim[0], args.xlim[1])
else:
ax1.set_xlim(min_progress_extrapolate, max_progress_extrapolate)
if args.ylim is not None:
ax1.set_ylim(args.ylim[0], args.ylim[1])
#
# Plot the percentile curves of the fitted model
#
if not args.no_model and not args.only_densities:
ax1.axvline(pm.min_progress, color='0.15', linestyle=':')
ax1.axvline(pm.max_progress, color='0.15', linestyle=':')
quantiles = [0.1, 0.25, 0.5, 0.75, 0.9]
grey_values = ['0.4', '0.2', '0', '0.2', '0.4']
for grey_value, quantile in zip(grey_values, quantiles):
curve_int = pm.get_quantile_curve(progress_linspace_int, quantile)
ax1.plot(progress_linspace_int, curve_int, color=grey_value)
if not args.no_extrapolation:
curve_ex1 = pm.get_quantile_curve(progress_linspace_ex1, quantile)
curve_ex2 = pm.get_quantile_curve(progress_linspace_ex2, quantile)
ax1.plot(progress_linspace_ex1, curve_ex1, '--', color=grey_value)
ax1.plot(progress_linspace_ex2, curve_ex2, '--', color=grey_value)
if args.plot_quantile_label:
label = '$q={0}\%$'.format(quantile * 100)
ax1.text(progress_linspace_int[-1] + 10, curve_int[-1], label, fontsize=10)
if args.plot_donohue:
print 'Plotting Donohue'
donohue_file = os.path.join(data_handler._conf.models_folder,
'donohue', 'population_{0}.csv'.format(biomarker.replace(' ', '.')))
if not os.path.isfile(donohue_file):
print log.ERROR, 'Donohue model file not found: {0}'.format(donohue_file)
return
r = mlab.csv2rec(donohue_file)
if args.method == 'joint':
offset = 2200
else:
offset = 300
progrs = r[r.dtype.names[0]] * 30.44 + offset
vals = r[r.dtype.names[1]]
curve_donohue = []
progr_donohue = []
for p in progress_linspace_int:
if progrs[0] < p < progrs[-1]:
i = 1
while p > progrs[i]:
i += 1
# TODO linear interpolation
progr_donohue.append(progrs[i])
curve_donohue.append(vals[i])
ax1.plot(progr_donohue, curve_donohue, '--', color='b', linewidth=2)
#
# Plot synthetic model curve
#
if plot_synth_model:
progress_linspace_synth = np.linspace(-2500, 2500, 100)
quantiles = [0.1, 0.25, 0.5, 0.75, 0.9]
alphas = [0.4, 0.7, 1.0, 0.7, 0.4]
for quantile, alpha in zip(quantiles, alphas):
curve_synth = [SynthModel.get_distributed_value(biomarker, p, cdf=quantile) for p in progress_linspace_synth]
ax1.plot(progress_linspace_synth, curve_synth, color='b', alpha=alpha)
#
# Plot predictor function
#
if args.plot_eta is not None and not args.only_densities:
# Get second axis of plot 1
ax1b = ax1.twinx()
# Plot all progresses
# ax1b.scatter(pm.all_progresses, pm.all_mus, facecolor='b', marker='o', edgecolor='none', alpha=0.2)
ax1b.text(pm.progresses[-1], pm.sigmas[-1], '$\mu$', color='b', fontsize=11)
# Plot binned progresses
ax1b.scatter(pm.progresses, pm.sigmas, color='b', marker='x')
# Plot interpolated model
mus = [pm.get_eta(pm.sigmas, p) for p in progress_linspace_int]
ax1b.plot(progress_linspace_int, mus, color='b')
if not args.no_extrapolation:
mus = [pm.get_eta(pm.sigmas, p) for p in progress_linspace_ex1]
ax1b.plot(progress_linspace_ex1, mus, '--', color='b')
mus = [pm.get_eta(pm.sigmas, p) for p in progress_linspace_ex2]
ax1b.plot(progress_linspace_ex2, mus, '--', color='b')
if args.xlim is not None:
ax1b.set_xlim(args.xlim[0], args.xlim[1])
else:
ax1b.set_xlim(min_progress_extrapolate, max_progress_extrapolate)
#
# Plot errors
#
if args.plot_errors and not args.only_densities:
eval_file = model_file.replace('.csv', '_eval_cover.csv')
if not os.path.isfile(eval_file):
print log.ERROR, 'Evaluation file not found: {0}'.format(eval_file)
else:
m = mlab.csv2rec(eval_file)
progresses = m['progress']
errors = m['error']
# Get second axis of plot 1
ax1b = ax1.twinx()
# ax1b.set_ylim(0, max(150, 1.2 * np.max(errors)))
ax1b.plot(progresses, errors, color='g', marker='x')
ax1b.text(progresses[-1], errors[-1], 'Discr.', color='g', fontsize=11)
ax1b.axhline(np.mean(errors), color='g', linestyle='--', alpha=0.5)
median_curve = pm.get_quantile_curve(progresses, 0.5)
min_value = np.min(median_curve)
max_value = np.max(median_curve)
rect = mpl.patches.Rectangle((progresses[0], min_value), progresses[-1] - progresses[0],
max_value - min_value,
fc=(0.0, 0.5, 0.0, 0.1), ec=(0.0, 0.5, 0.0, 0.8),
linewidth=1)
ax1.add_patch(rect)
#
# Plot points
#
if not args.no_points and not args.only_densities:
samples_file = data_handler.get_samples_file(biomarker)
if not os.path.isfile(samples_file):
print log.ERROR, 'Samples file not found: {0}'.format(samples_file)
else:
m = mlab.csv2rec(samples_file)
progr_points = m['progress']
value_points = m['value']
# diagn_points = [0.5 if p < 0 else 1.0 for p in progr_points]
diagn_points = m['diagnosis']
diagn_points[(0.25 <= diagn_points) & (diagn_points <= 0.75)] = 0.5
print log.INFO, 'Plotting {0} sample points...'.format(len(progr_points))
ax1.scatter(progr_points, value_points, s=15.0, c=diagn_points, edgecolor='none',
vmin=0.0, vmax=1.0, cmap=pt.progression_cmap, alpha=args.points_alpha)
if args.phase == 'cnmci':
rects = [mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_cn + (args.points_alpha,), linewidth=0),
mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_mci + (args.points_alpha,), linewidth=0)]
labels = ['CN', 'MCI']
elif args.phase == 'mciad':
rects = [mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_mci + (args.points_alpha,), linewidth=0),
mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_ad + (args.points_alpha,), linewidth=0)]
labels = ['MCI', 'AD']
else:
rects = [mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_cn + (args.points_alpha,), linewidth=0),
mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_mci + (args.points_alpha,), linewidth=0),
mpl.patches.Rectangle((0, 0), 1, 1, fc=pt.color_ad + (args.points_alpha,), linewidth=0)]
labels = ['CN', 'MCI', 'AD']
legend = ax1.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))
#
# Plot PDFs
#
progr_samples = [-2000, -1000, 0, 1000, 2000, 3000, 4000] if args.phase == 'joint' else \
[-2000, -1500, -1000, -500, 0, 500, 1000, 1500, 2000]
if args.phase == 'cnmci':
vmin = -2000
vmax = 6000
elif args.phase == 'mciad':
vmin = -6000
vmax = 2000
elif args.phase == 'joint':
vmin = -2000
vmax = 4000
sample_cmap = cmx.ScalarMappable(
norm=colors.Normalize(vmin=vmin, vmax=vmax),
cmap=plt.get_cmap(pt.progression_cmap))
if not args.no_sample_lines and not args.only_densities:
for progr in progr_samples:
if not args.no_extrapolation or pm.min_progress < progr < pm.max_progress:
# sample_color = sample_cmap.to_rgba(progr_samples.index(progr))
sample_color = sample_cmap.to_rgba(progr)
linestyle = '--' if progr < pm.min_progress or progr > pm.max_progress else '-'
ax1.axvline(progr, color=sample_color, linestyle=linestyle, alpha=0.3)
if not args.no_densities:
ax2.set_title('Probability density function for {0}'.format(biomarker_string))
ax2.set_xlabel(DataHandler.get_biomarker_unit(biomarker))
ax2.set_ylabel('Probability')
if args.ylim is None:
values = np.linspace(min_val, max_val, 250)
ax2.set_xlim(min_val, max_val)
else:
values = np.linspace(args.ylim[0], args.ylim[1], 250)
ax2.set_xlim(args.ylim[0], args.ylim[1])
for progr in progr_samples:
if not args.no_extrapolation or pm.min_progress < progr < pm.max_progress:
# sample_color = sample_cmap.to_rgba(progr_samples.index(progr))
sample_color = sample_cmap.to_rgba(progr)
linestyle = '--' if progr < pm.min_progress or progr > pm.max_progress else '-'
probs = pm.get_density_distribution(values, progr)
ax2.plot(values, probs, label=str(progr), color=sample_color, linestyle=linestyle)
if plot_synth_model:
probs = [SynthModel.get_probability(biomarker, progr, v) for v in values]
ax2.plot(values, probs, color='b', linestyle='--')
legend = ax2.legend(fontsize=10, loc='best', framealpha=0.9)
legend.get_frame().set_edgecolor((0.6, 0.6, 0.6))
#
# Draw or save the plot
#
plt.tight_layout()
if args.save_plots or args.plot_file is not None:
if args.plot_file is not None:
plot_filename = args.plot_file
else:
plot_filename = model_file.replace('.csv', '.pdf')
plt.savefig(plot_filename, transparent=True)
else:
plt.show()
plt.close(fig)