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)
def test_calc():
prediction = numpy.random.random(10000)
iron = utils.Flattener(prediction)
assert numpy.allclose(numpy.histogram(iron(prediction),
normed=True,
bins=30)[0],
numpy.ones(30),
rtol=1e-02)
x, y, yerr, xerr = utils.calc_hist_with_errors(iron(prediction),
bins=30,
x_range=(0, 1))
assert numpy.allclose(y, numpy.ones(len(y)), rtol=1e-02)
width = 1. / 60
means = numpy.linspace(width, 1 - width, 30)
assert numpy.allclose(x, means)
assert numpy.allclose(xerr, numpy.zeros(len(xerr)) + width)
assert numpy.allclose(yerr, numpy.zeros(len(yerr)) + yerr[0], rtol=1e-2)
random_labels = numpy.random.choice(2, size=10000)
(tpr, tnr), _, _ = utils.calc_ROC(prediction, random_labels)
# checking for random classifier
assert numpy.max(abs(1 - tpr - tnr)) < 0.05
# checking efficiencies for random mass, random prediction
mass = numpy.random.random(10000)
result = utils.get_efficiencies(prediction, mass)
for threshold, (xval, yval) in result.items():
assert ((yval + threshold - 1)**2).mean() < 0.1
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)
def test_calc():
prediction = numpy.random.random(10000)
iron = utils.Flattener(prediction)
assert numpy.allclose(numpy.histogram(iron(prediction), normed=True, bins=30)[0], numpy.ones(30), rtol=1e-02)
x, y, y_err, x_err = utils.calc_hist_with_errors(iron(prediction), bins=30, x_range=(0, 1))
assert numpy.allclose(y, numpy.ones(len(y)), rtol=1e-02)
width = 1. / 60
means = numpy.linspace(width, 1 - width, 30)
assert numpy.allclose(x, means)
assert numpy.allclose(x_err, numpy.zeros(len(x_err)) + width)
assert numpy.allclose(y_err, numpy.zeros(len(y_err)) + y_err[0], rtol=1e-2)
random_labels = numpy.random.choice(2, size=10000)
(tpr, tnr), _, _ = utils.calc_ROC(prediction, random_labels)
# checking for random classifier
assert numpy.max(abs(1 - tpr - tnr)) < 0.05
# checking efficiencies for random mass, random prediction
mass = numpy.random.random(10000)
result = utils.get_efficiencies(prediction, mass)
for threshold, (xval, yval) in result.items():
assert ((yval + threshold - 1) ** 2).mean() < 0.1
def plot_flatness_by_particle(labels,
predictions,
spectator,
spectator_name,
predictions_comparison=None,
names_algorithms=['MVA', 'Baseline'],
for_particle=None,
weights=None,
bins_number=30,
ignored_sideband=0.1,
thresholds=None,
n_col=1):
"""
Build a flatness-plot, which demonstrates the dependency between efficiency and some observable.
:param labels: [n_samples], contains targets
:param predictions: [n_samples, n_particle_types] with predictions of an algorithm
:param spectator: [n_samples], values of spectator variable
:param spectator_name: str, name shown on the plot
:param predictions_comparison: [n_samples, n_particle types], optionally for comparison this may be provided
:param names_algorithms: names for compared algorithms
:param weights: [n_samples], optional
:param bins_number: int,
:param ignored_sideband: fraction of ignored sidebands
:param thresholds: efficiencies, for which flatness is drawn
:param n_col: number of columns in legend.
"""
plt.figure(figsize=(22, 24))
if predictions_comparison is not None:
colors = ['blue', 'green']
markers = ['o', 's', 'v', 'o', 's', 'v']
else:
colors = [None, None]
markers = ['o'] * len(thresholds)
for n, (particle_name,
label) in enumerate(names_labels_correspondence.items()):
plt.subplot(3, 2, n + 1)
title = '{} algorithm'.format(particle_name)
xlim_all = (1e10, -1e10)
ylim_all = (20, -1e8)
legends = []
for preds, algo_name, color in zip(
[predictions, predictions_comparison], names_algorithms, colors):
if preds is None:
continue
particle_mask = labels == label
particle_probs = preds[particle_mask, label]
particle_weights = None if weights is None else weights[
particle_mask]
thresholds_values = [
weighted_quantile(particle_probs,
quantiles=1 - eff / 100.,
sample_weight=particle_weights)
for eff in thresholds
]
if for_particle is not None:
particle_mask = labels == names_labels_correspondence[
for_particle]
particle_probs = preds[particle_mask, label]
particle_weights = None if weights is None else weights[
particle_mask]
title = '{} algorithm for {}'.format(particle_name,
for_particle)
eff = get_efficiencies(particle_probs,
spectator[particle_mask],
sample_weight=particle_weights,
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])
xlim, ylim = compute_limits_and_plot_errorbar(eff,
markers,
color=color)
plt.xlabel('{} {}\n\n'.format(particle_name, spectator_name),
fontsize=22)
plt.ylabel('Efficiency', fontsize=22)
plt.title('\n\n'.format(title), fontsize=22)
plt.xticks(fontsize=12), plt.yticks(fontsize=12)
legends.append(
['{} Eff {}%'.format(algo_name, thr) for thr in thresholds])
plt.grid(True)
xlim_all = (min(xlim_all[0], xlim[0]), max(xlim_all[1], xlim[1]))
ylim_all = (min(ylim_all[0], ylim[0]), max(ylim_all[1], ylim[1]))
plt.legend(numpy.concatenate(legends),
loc='best',
fontsize=16,
framealpha=0.5,
ncol=n_col)
plt.xlim(xlim_all[0], xlim_all[1])
plt.ylim(ylim_all[0], ylim_all[1])
def flatness_eta_figure(proba,
proba_baseline,
eta,
track_name,
particle_name,
save_path=None,
show=False):
"""
Plot signal efficiency vs pseudo rapidity figure.
Parameters
----------
proba : array_like
Predicted probabilities with array shape = [n_samples].
probas_baseline : array_like
Baseline predicted probabilities with array shape = [n_samples].
eta : array_like
Pseudo rapidity values with array shape = [n_samples].
track_name : string
The track name.
particle_name : string
The particle name.
save_path : string
Path to a directory where the figure will saved. If None the figure will not be saved.
show : boolean
If true the figure will be displayed.
"""
thresholds = numpy.percentile(proba, 100 - numpy.array([20, 50, 80]))
thresholds_baseline = numpy.percentile(proba_baseline,
100 - numpy.array([20, 50, 80]))
eff = get_efficiencies(proba,
eta,
bins_number=30,
errors=True,
ignored_sideband=0.005,
thresholds=thresholds)
eff_baseline = get_efficiencies(proba_baseline,
eta,
bins_number=30,
errors=True,
ignored_sideband=0.005,
thresholds=thresholds_baseline)
for i in thresholds:
eff[i] = (eff[i][0], 100. * eff[i][1], 100. * eff[i][2], eff[i][3])
for i in thresholds_baseline:
eff_baseline[i] = (eff_baseline[i][0], 100. * eff_baseline[i][1],
100. * eff_baseline[i][2], eff_baseline[i][3])
eff_total = OrderedDict()
num = len(eff) + len(eff_baseline)
for i in range(len(eff)):
v = eff[eff.keys()[i]]
v_baseline = eff_baseline[eff_baseline.keys()[i]]
eff_total[num] = v
eff_total[num - 1] = v_baseline
num += -2
plot_fig = ErrorPlot(eff_total)
plot_fig.ylim = (0, 100)
plot_fig.plot(new_plot=True, figsize=(10, 7))
labels = [
'Eff model = 20 %', 'Eff baseline = 20 %', 'Eff model = 50 %',
'Eff baseline = 50 %', 'Eff model = 80 %', 'Eff baseline = 80 %'
]
plt.legend(labels, loc='best', prop={'size': 10}, framealpha=0.5, ncol=3)
plt.xlabel(track_name + ' ' + particle_name + ' Pseudo Rapidity', size=15)
plt.xticks(size=15)
plt.ylabel('Efficiency / %', size=15)
plt.yticks(size=15)
plt.title('Flatness_SignalMVAEffVPseudoRapidity_' + track_name + ' ' +
particle_name,
size=15)
if save_path != None:
plt.savefig(save_path + "/" + 'Flatness_SignalMVAEffVPseudoRapidity_' +
track_name + '_' + particle_name + ".png")
if show == True:
plt.show()
plt.clf()
plt.close()
def flatness_eta_figure(proba, proba_baseline, eta, track_name, particle_name, save_path=None, show=False):
"""
Plot signal efficiency vs pseudo rapidity figure.
Parameters
----------
proba : array_like
Predicted probabilities with array shape = [n_samples].
probas_baseline : array_like
Baseline predicted probabilities with array shape = [n_samples].
eta : array_like
Pseudo rapidity values with array shape = [n_samples].
track_name : string
The track name.
particle_name : string
The particle name.
save_path : string
Path to a directory where the figure will saved. If None the figure will not be saved.
show : boolean
If true the figure will be displayed.
"""
thresholds = numpy.percentile(proba, 100 - numpy.array([20, 50, 80]))
thresholds_baseline = numpy.percentile(proba_baseline, 100 - numpy.array([20, 50, 80]))
eff = get_efficiencies(proba,
eta,
bins_number=30,
errors=True,
ignored_sideband=0.005,
thresholds=thresholds)
eff_baseline = get_efficiencies(proba_baseline,
eta,
bins_number=30,
errors=True,
ignored_sideband=0.005,
thresholds=thresholds_baseline)
for i in thresholds:
eff[i] = (eff[i][0], 100. * eff[i][1], 100. * eff[i][2], eff[i][3])
for i in thresholds_baseline:
eff_baseline[i] = (eff_baseline[i][0], 100. * eff_baseline[i][1], 100. * eff_baseline[i][2], eff_baseline[i][3])
eff_total = OrderedDict()
num = len(eff) + len(eff_baseline)
for i in range(len(eff)):
v = eff[eff.keys()[i]]
v_baseline = eff_baseline[eff_baseline.keys()[i]]
eff_total[num] = v
eff_total[num - 1] = v_baseline
num += -2
plot_fig = ErrorPlot(eff_total)
plot_fig.ylim = (0, 100)
plot_fig.plot(new_plot=True, figsize=(10,7))
labels = ['Eff model = 20 %', 'Eff baseline = 20 %',
'Eff model = 50 %', 'Eff baseline = 50 %',
'Eff model = 80 %', 'Eff baseline = 80 %']
plt.legend(labels, loc='best',prop={'size':10}, framealpha=0.5, ncol=3)
plt.xlabel(track_name + ' ' + particle_name + ' Pseudo Rapidity', size=15)
plt.xticks(size=15)
plt.ylabel('Efficiency / %', size=15)
plt.yticks(size=15)
plt.title('Flatness_SignalMVAEffVPseudoRapidity_' + track_name + ' ' + particle_name, size=15)
if save_path != None:
plt.savefig(save_path + "/" + 'Flatness_SignalMVAEffVPseudoRapidity_' + track_name + '_' + particle_name + ".png")
if show == True:
plt.show()
plt.clf()
plt.close()
