RejectionOverEfficiency Class Reference

Inheritance diagram for RejectionOverEfficiency:

Collaboration diagram for RejectionOverEfficiency:

Public Member Functions
def	add (self, data, column, signal_mask, bckgrd_mask, weight_column=None, label=None)
def	finish (self)

Public Attributes
	xmax
	Maximum x value.
	ymax
	Maximum y value.

Detailed Description

Plots the rejection over the efficiency also known as ROC curve

Definition at line 425 of file plotting.py.

Member Function Documentation

add()

def add	(		self,
			data,
			column,
			signal_mask,
			bckgrd_mask,
			weight_column = None,
			label = None
	)

Add a new curve to the ROC plot
@param data pandas.DataFrame containing all data
@param column which is used to calculate efficiency and purity for different cuts
@param signal_mask boolean numpy.array defining which events are signal events
@param bckgrd_mask boolean numpy.array defining which events are background events
@param weight_column column in data containing the weights for each event

Reimplemented from Plotter.

Definition at line 434 of file plotting.py.

    def add(self, data, column, signal_mask, bckgrd_mask, weight_column=None, label=None):
        """
        Add a new curve to the ROC plot
        @param data pandas.DataFrame containing all data
        @param column which is used to calculate efficiency and purity for different cuts
        @param signal_mask boolean numpy.array defining which events are signal events
        @param bckgrd_mask boolean numpy.array defining which events are background events
        @param weight_column column in data containing the weights for each event
        """
        hists = histogram.Histograms(data, column, {'Signal': signal_mask, 'Background': bckgrd_mask}, weight_column=weight_column)
        efficiency, efficiency_error = hists.get_efficiency(['Signal'])
        rejection, rejection_error = hists.get_efficiency(['Background'])
        rejection = 1 - rejection
        if isinstance(efficiency, int) and not isinstance(rejection, int):
            efficiency = numpy.array([efficiency] * len(rejection))
        elif isinstance(rejection, int) and not isinstance(efficiency, int):
            rejection = numpy.array([rejection] * len(efficiency))
        elif isinstance(rejection, int) and isinstance(efficiency, int):
            efficiency = numpy.array([efficiency])
            rejection = numpy.array([rejection])
 
        self.xmin, self.xmax = numpy.nanmin([efficiency.min(), self.xmin]), numpy.nanmax([efficiency.max(), self.xmax])
        self.ymin, self.ymax = numpy.nanmin([rejection.min(), self.ymin]), numpy.nanmax([rejection.max(), self.ymax])
 
        auc = numpy.abs(numpy.trapz(rejection, efficiency))
 
        p = self._plot_datapoints(self.axis, efficiency, rejection, xerr=efficiency_error, yerr=rejection_error)
        self.plots.append(p)
        if label is not None:
            self.labels.append(label[:10] + f" ({auc:.2f})")
        else:
            self.labels.append(column[:10] + f" ({auc:.2f})")
        return self
 

finish()

def finish

(

self

)

Sets limits, title, axis-labels and legend of the plot

Reimplemented from Plotter.

Definition at line 468 of file plotting.py.

    def finish(self):
        """
        Sets limits, title, axis-labels and legend of the plot
        """
        self.axis.set_xlim((self.xmin, self.xmax))
        self.axis.set_ylim((self.ymin, self.ymax))
        self.axis.set_title("ROC Rejection Plot")
        self.axis.get_xaxis().set_label_text('Signal Efficiency')
        self.axis.get_yaxis().set_label_text('Background Rejection')
        self.axis.legend([x[0] for x in self.plots], self.labels, loc='best', fancybox=True, framealpha=0.5)
        return self
 
 

Member Data Documentation

xmax

xmax

Maximum x value.

Definition at line 455 of file plotting.py.

ymax

ymax

Maximum y value.

Definition at line 456 of file plotting.py.

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The documentation for this class was generated from the following file:

• mva/scripts/basf2_mva_evaluation/plotting.py