CutAtBackgroundRejectionClassifier Class Reference

Inheritance diagram for CutAtBackgroundRejectionClassifier:

Public Member Functions
	__init__ (self, background_rejection=0.5, cut_direction=1)
	determine_cut_value (self, estimates, truths)
	cut_direction (self)
	cut_value (self)
	clone (self)
	fit (self, estimates, truths)
	predict (self, estimates)
	describe (self, estimates, truths)

Public Attributes
	background_rejection = background_rejection
	cachec copy of the background-rejection threshold
	cut_direction_ = cut_direction
	cached copy of the cut direction (< or >)
	cut_value_ = cut_value
	cached copy of the cut threshold

Detailed Description

Apply cut on the background rejection

Definition at line 564 of file classification.py.

Constructor & Destructor Documentation

__init__()

__init__	(		self,
			background_rejection = 0.5,
			cut_direction = 1 )

Constructor

Definition at line 567 of file classification.py.

    def __init__(self, background_rejection=0.5, cut_direction=1):
        """Constructor"""
        super().__init__(cut_direction=cut_direction, cut_value=np.nan)
        ## cachec copy of the background-rejection threshold
        self.background_rejection = background_rejection
 

Member Function Documentation

clone()

clone ( self )

inherited

Return a clone of this object

Definition at line 517 of file classification.py.

    def clone(self):
        """Return a clone of this object"""
        return copy.copy(self)
 

cut_direction()

cut_direction ( self )

inherited

Get the value of the cut direction

Definition at line 508 of file classification.py.

    def cut_direction(self):
        """Get the value of the cut direction"""
        return self.cut_direction_
 

cut_value()

cut_value ( self )

inherited

Get the value of the cut threshold

Definition at line 513 of file classification.py.

    def cut_value(self):
        """Get the value of the cut threshold"""
        return self.cut_value_
 

describe()

describe ( self, estimates, truths )

inherited

Describe the cut selection and its efficiency, purity and background rejection

Definition at line 542 of file classification.py.

    def describe(self, estimates, truths):
        """Describe the cut selection and its efficiency, purity and background rejection"""
        if self.cut_direction_ < 0:
            print("Cut accepts >= ", self.cut_value_, 'with')
        else:
            print("Cut accepts <= ", self.cut_value_, 'with')
 
        binary_estimates = self.predict(estimates)
 
        efficiency = scores.efficiency(truths, binary_estimates)
        purity = scores.purity(truths, binary_estimates)
        background_rejection = scores.background_rejection(truths, binary_estimates)
 
        print("efficiency", efficiency)
        print("purity", purity)
        print("background_rejection", background_rejection)
 
 

determine_cut_value()

determine_cut_value	(		self,
			estimates,
			truths )

Find the cut value that satisfies the desired background-rejection level

Reimplemented from CutClassifier.

Definition at line 573 of file classification.py.

    def determine_cut_value(self, estimates, truths):
        """Find the cut value that satisfies the desired background-rejection level"""
        n_data = len(estimates)
        n_signals = scores.signal_amount(truths, estimates)
        n_bkgs = n_data - n_signals
 
        sorting_indices = np.argsort(estimates)
        if self.cut_direction_ < 0:  # reject low
            # Keep a reference to keep the content alive
            original_sorting_indices = sorting_indices  # noqa
            sorting_indices = sorting_indices[::-1]
 
        sorted_truths = truths[sorting_indices]
        sorted_estimates = estimates[sorting_indices]
 
        sorted_n_accepted_signals = np.cumsum(sorted_truths, dtype=float)
        # sorted_efficiencies = sorted_n_accepted_signals / n_signals
 
        sorted_n_rejected_signals = n_signals - sorted_n_accepted_signals
        sorted_n_rejects = np.arange(len(estimates) + 1, 1, -1)
        sorted_n_rejected_bkgs = sorted_n_rejects - sorted_n_rejected_signals
        sorted_bkg_rejections = sorted_n_rejected_bkgs / n_bkgs
 
        cut_index, = np.searchsorted(sorted_bkg_rejections[::-1], (self.background_rejection,), side='right')
 
        cut_value = sorted_estimates[-cut_index - 1]
        return cut_value

fit()

fit ( self, estimates, truths )

inherited

Fit to determine the cut threshold

Definition at line 525 of file classification.py.

    def fit(self, estimates, truths):
        """Fit to determine the cut threshold"""
        self.cut_value_ = self.determine_cut_value(estimates, truths)
        return self
 

predict()

predict ( self, estimates )

inherited

Select estimates that satisfy the cut

Definition at line 530 of file classification.py.

    def predict(self, estimates):
        """Select estimates that satisfy the cut"""
        if self.cut_value_ is None:
            raise ValueError("Cut value not set. Forgot to fit?")
 
        if self.cut_direction_ < 0:
            binary_estimates = estimates >= self.cut_value_
        else:
            binary_estimates = estimates <= self.cut_value_
 
        return binary_estimates
 

Member Data Documentation

background_rejection

background_rejection = background_rejection

cachec copy of the background-rejection threshold

Definition at line 571 of file classification.py.

cut_direction_

cut_direction_ = cut_direction

inherited

cached copy of the cut direction (< or >)

Definition at line 503 of file classification.py.

cut_value_

cut_value_ = cut_value

inherited

cached copy of the cut threshold

Definition at line 505 of file classification.py.

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

• tracking/scripts/tracking/validation/classification.py