def get_efficiencies(prediction,
spectator,
sample_weight=None,
bins_number=20,
thresholds=None,
errors=False,
ignored_sideband=0.0):
prediction, spectator, sample_weight = \
check_arrays(prediction, spectator, sample_weight)
spectator_min, spectator_max = weighted_quantile(
spectator, [ignored_sideband, (1. - ignored_sideband)])
mask = (spectator >= spectator_min) & (spectator <= spectator_max)
spectator = spectator[mask]
prediction = prediction[mask]
bins_number = min(bins_number, len(prediction))
sample_weight = sample_weight if sample_weight is None else numpy.array(
sample_weight)[mask]
if thresholds is None:
thresholds = [
weighted_quantile(prediction,
quantiles=1 - eff,
sample_weight=sample_weight)
for eff in [0.2, 0.4, 0.5, 0.6, 0.8]
]
binner = Binner(spectator, bins_number=bins_number)
if sample_weight is None:
sample_weight = numpy.ones(len(prediction))
bins_data = binner.split_into_bins(spectator, prediction,
sample_weight)
bin_edges = numpy.array([spectator_min] + list(binner.limits) +
[spectator_max])
xerr = numpy.diff(bin_edges) / 2.
result = OrderedDict()
for threshold in thresholds:
x_values = []
y_values = []
N_in_bin = []
for num, (masses, probabilities, weights) in enumerate(bins_data):
if len(weights) == 0 or sum(weights) == 0.0: continue
y_values.append(
numpy.average(probabilities > threshold, weights=weights))
N_in_bin.append(numpy.sum(weights))
if errors:
x_values.append((bin_edges[num + 1] + bin_edges[num]) / 2.)
else:
x_values.append(numpy.mean(masses))
x_values, y_values, N_in_bin = check_arrays(
x_values, y_values, N_in_bin)
if errors:
result[threshold] = (x_values, y_values,
numpy.sqrt(y_values * (1 - y_values) /
N_in_bin), xerr)
else:
result[threshold] = (x_values, y_values)
return result
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 test_weighted_quantile(size=10000):
x = numpy.random.normal(size=size)
weights = numpy.random.random(size=size)
quantile_level = numpy.random.random()
quantile_value = utils.weighted_quantile(x, quantile_level, sample_weight=weights)
passed_weight = numpy.sum((x < quantile_value) * weights)
expected_weight = quantile_level * numpy.sum(weights)
assert numpy.abs(passed_weight - expected_weight) < 1.1, 'wrong cut'
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 get_profiles(prediction,
spectator,
sample_weight=None,
bins_number=20,
errors=False,
ignored_sideband=0.0):
"""
Construct profile of prediction vs. spectator
:param binner: Binner object with bins computed from combined sig+bkg spectator value list
:param prediction: list of probabilities
:param spectator: list of spectator's values
:param bins_number: int, count of bins for plot
:return:
if errors=False
tuple (x_values, y_values)
if errors=True
tuple (x_values, y_values, y_err, x_err)
All the parts: x_values, y_values, y_err, x_err are numpy.arrays of the same length.
"""
prediction, spectator, sample_weight = check_arrays(
prediction, spectator, sample_weight)
spectator_min, spectator_max = weighted_quantile(
spectator, [ignored_sideband, (1. - ignored_sideband)])
mask = (spectator >= spectator_min) & (spectator <= spectator_max)
spectator = spectator[mask]
prediction = prediction[mask]
bins_number = min(bins_number, len(prediction))
sample_weight = sample_weight if sample_weight is None else numpy.array(
sample_weight)[mask]
binner = Binner(spectator, bins_number=bins_number)
if sample_weight is None:
sample_weight = numpy.ones(len(prediction))
bins_data = binner.split_into_bins(spectator, prediction,
sample_weight)
bin_edges = numpy.array([spectator_min] + list(binner.limits) +
[spectator_max])
x_err = numpy.diff(bin_edges) / 2.
result = OrderedDict()
x_values = []
y_values = []
N_in_bin = []
y_err = []
for num, (masses, probabilities, weights) in enumerate(bins_data):
y_values.append(
numpy.average(probabilities, weights=weights)
if len(weights) > 0 and sum(weights) > 0.0 else 0)
y_err.append(
numpy.sqrt(
numpy.
cov(probabilities, aweights=numpy.abs(weights), ddof=0) /
numpy.sum(weights)
) if len(weights) > 0 and sum(weights) > 0.0 else 0)
N_in_bin.append(numpy.sum(weights))
x_values.append((bin_edges[num + 1] + bin_edges[num]) / 2.)
x_values, y_values, N_in_bin = check_arrays(x_values, y_values,
N_in_bin)
if errors:
return (x_values, y_values, y_err, x_err)
else:
return (x_values, y_values)
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])
