def plot_flatness_particle(labels, predictions_dict, spectator, spectator_name, particle_name,
weights=None, bins_number=30, ignored_sideband=0.1,
thresholds=None, cuts_values=False):
plt.figure(figsize=(18, 22))
for n, (name, label) in enumerate(names_labels_correspondence.items()):
plt.subplot(3, 2, n + 1)
mask = labels == names_labels_correspondence[particle_name]
probs = predictions_dict[label][mask]
mask_signal = labels == label
probs_signal = predictions_dict[label][mask_signal]
if cuts_values:
thresholds_values = cut_values
else:
thresholds_values = [weighted_quantile(probs_signal, quantiles=1 - eff / 100.,
sample_weight=None if weights is None else weights[mask_signal])
for eff in thresholds]
eff = get_efficiencies(probs, spectator[mask],
sample_weight=None if weights is None else weights[mask],
bins_number=bins_number, errors=True, ignored_sideband=ignored_sideband,
thresholds=thresholds_values)
for thr in thresholds_values:
eff[thr] = (eff[thr][0], 100*numpy.array(eff[thr][1]), 100*numpy.array(eff[thr][2]), eff[thr][3])
plot_fig = ErrorPlot(eff)
plot_fig.xlabel = '{} {}'.format(particle_name, spectator_name)
plot_fig.ylabel = 'Efficiency'
plot_fig.title = 'MVA {}'.format(name)
plot_fig.ylim = (0, 100)
plot_fig.plot(fontsize=22)
plt.xticks(fontsize=12), plt.yticks(fontsize=12)
if not cuts_values:
plt.legend(['Signal Eff {}%'.format(thr) for thr in thresholds], loc='best', fontsize=18, framealpha=0.5)
def plot_flatness_by_particle(labels, predictions_dict, spectator, spectator_name, predictions_dict_comparison=None,
names_algorithms=['MVA', 'Baseline'],
weights=None, bins_number=30, ignored_sideband=0.1,
thresholds=None, cuts_values=False, ncol=1):
plt.figure(figsize=(22, 20))
for n, (name, label) in enumerate(names_labels_correspondence.items()):
plt.subplot(3, 2, n + 1)
mask =labels == label
legends = []
for preds, name_algo in zip([predictions_dict, predictions_dict_comparison], names_algorithms):
if preds is None:
continue
probs = preds[label][mask]
if cuts_values:
thresholds_values = cut_values
else:
thresholds_values = [weighted_quantile(probs, quantiles=1 - eff / 100.,
sample_weight=None if weights is None else weights[mask])
for eff in thresholds]
eff = get_efficiencies(probs, spectator[mask],
sample_weight=None if weights is None else weights[mask],
bins_number=bins_number, errors=True, ignored_sideband=ignored_sideband,
thresholds=thresholds_values)
for thr in thresholds_values:
eff[thr] = (eff[thr][0], 100*numpy.array(eff[thr][1]), 100*numpy.array(eff[thr][2]), eff[thr][3])
plot_fig = ErrorPlot(eff)
plot_fig.xlabel = '{} {}'.format(name, spectator_name)
plot_fig.ylabel = 'Efficiency'
plot_fig.title = name
plot_fig.ylim = (0, 100)
plot_fig.plot(fontsize=22)
plt.xticks(fontsize=12), plt.yticks(fontsize=12)
legends.append(['{} Eff {}%'.format(thr, name_algo) for thr in thresholds])
plt.legend(numpy.concatenate(legends), loc='best', fontsize=12, framealpha=0.5, ncol=ncol)
def plot_flatness_by_particle(labels,
predictions_dict,
spectator,
spectator_name,
predictions_dict_comparison=None,
names_algorithms=['MVA', 'Baseline'],
weights=None,
bins_number=30,
ignored_sideband=0.1,
thresholds=None,
cuts_values=False,
ncol=1):
plt.figure(figsize=(22, 20))
for n, (name, label) in enumerate(names_labels_correspondence.items()):
plt.subplot(3, 2, n + 1)
mask = labels == label
legends = []
for preds, name_algo in zip(
[predictions_dict, predictions_dict_comparison], names_algorithms):
if preds is None:
continue
probs = preds[label][mask]
if cuts_values:
thresholds_values = cut_values
else:
thresholds_values = [
weighted_quantile(probs,
quantiles=1 - eff / 100.,
sample_weight=None
if weights is None else weights[mask])
for eff in thresholds
]
eff = get_efficiencies(
probs,
spectator[mask],
sample_weight=None if weights is None else weights[mask],
bins_number=bins_number,
errors=True,
ignored_sideband=ignored_sideband,
thresholds=thresholds_values)
for thr in thresholds_values:
eff[thr] = (eff[thr][0], 100 * numpy.array(eff[thr][1]),
100 * numpy.array(eff[thr][2]), eff[thr][3])
plot_fig = ErrorPlot(eff)
plot_fig.xlabel = '{} {}'.format(name, spectator_name)
plot_fig.ylabel = 'Efficiency'
plot_fig.title = name
plot_fig.ylim = (0, 100)
plot_fig.plot(fontsize=22)
plt.xticks(fontsize=12), plt.yticks(fontsize=12)
legends.append(
['{} Eff {}%'.format(thr, name_algo) for thr in thresholds])
plt.legend(numpy.concatenate(legends),
loc='best',
fontsize=12,
framealpha=0.5,
ncol=ncol)
def plot_flatness_particle(labels,
predictions_dict,
spectator,
spectator_name,
particle_name,
weights=None,
bins_number=30,
ignored_sideband=0.1,
thresholds=None,
cuts_values=False):
plt.figure(figsize=(18, 22))
for n, (name, label) in enumerate(names_labels_correspondence.items()):
plt.subplot(3, 2, n + 1)
mask = labels == names_labels_correspondence[particle_name]
probs = predictions_dict[label][mask]
mask_signal = labels == label
probs_signal = predictions_dict[label][mask_signal]
if cuts_values:
thresholds_values = cut_values
else:
thresholds_values = [
weighted_quantile(probs_signal,
quantiles=1 - eff / 100.,
sample_weight=None
if weights is None else weights[mask_signal])
for eff in thresholds
]
eff = get_efficiencies(
probs,
spectator[mask],
sample_weight=None if weights is None else weights[mask],
bins_number=bins_number,
errors=True,
ignored_sideband=ignored_sideband,
thresholds=thresholds_values)
for thr in thresholds_values:
eff[thr] = (eff[thr][0], 100 * numpy.array(eff[thr][1]),
100 * numpy.array(eff[thr][2]), eff[thr][3])
plot_fig = ErrorPlot(eff)
plot_fig.xlabel = '{} {}'.format(particle_name, spectator_name)
plot_fig.ylabel = 'Efficiency'
plot_fig.title = 'MVA {}'.format(name)
plot_fig.ylim = (0, 100)
plot_fig.plot(fontsize=22)
plt.xticks(fontsize=12), plt.yticks(fontsize=12)
if not cuts_values:
plt.legend(['Signal Eff {}%'.format(thr) for thr in thresholds],
loc='best',
fontsize=18,
framealpha=0.5)
