PurityAndEfficiencyOverCut Class Reference

Inheritance diagram for PurityAndEfficiencyOverCut:

Collaboration diagram for PurityAndEfficiencyOverCut:

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

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

Detailed Description

Plots the purity and the efficiency over the cut value (for cut choosing)

Definition at line 277 of file plotting.py.

Member Function Documentation

add()

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

Add a new curve to the 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 286 of file plotting.py.

    def add(self, data, column, signal_mask, bckgrd_mask, weight_column=None, normed=True):
        """
        Add a new curve to the 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)
 
        if normed:
            efficiency, efficiency_error = hists.get_efficiency(['Signal'])
            purity, purity_error = hists.get_purity(['Signal'], ['Background'])
        else:
            efficiency, efficiency_error = hists.get_true_positives(['Signal'])
            purity, purity_error = hists.get_false_positives(['Background'])
 
        cuts = hists.bin_centers
 
        self.xmin, self.xmax = numpy.nanmin([numpy.nanmin(cuts), self.xmin]), numpy.nanmax([numpy.nanmax(cuts), self.xmax])
        self.ymin, self.ymax = numpy.nanmin([numpy.nanmin(efficiency), numpy.nanmin(purity), self.ymin]), \
            numpy.nanmax([numpy.nanmax(efficiency), numpy.nanmax(purity), self.ymax])
 
        self.plots.append(self._plot_datapoints(self.axis, cuts, efficiency, xerr=0, yerr=efficiency_error))
 
        if normed:
            self.labels.append("Efficiency")
        else:
            self.labels.append("True positive")
 
        self.plots.append(self._plot_datapoints(self.axis, cuts, purity, xerr=0, yerr=purity_error))
 
        if normed:
            self.labels.append("Purity")
        else:
            self.labels.append("False positive")
 
        return self
 

finish()

def finish

(

self

)

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

Reimplemented from Plotter.

Definition at line 327 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("Classification Plot")
        self.axis.get_xaxis().set_label_text('Cut Value')
        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 307 of file plotting.py.

ymax

ymax

Maximum y value.

Definition at line 308 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