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)