Priors Class Reference

Public Member Functions
def	__init__ (self, list particlelist, str Model, str prescaling=None)
def	calculate_priors (self, np.array momentum, np.array cosTheta)
np.array	get_priors (self, int pdg=None)
np.array	get_posterior (self, int pid, int pdg=None)

Public Attributes
	model
	The torch model for prior calculation.
	scale
	Temperature scaling object for calibration.
	require_scale
	True if the scaling object exist. More...
	plist
	Sorted particle PDG list.
	prior
	Numpy array containing PID prior probability data.

Detailed Description

Class to calculate PID prior probabilities and posteriors.

Attributes:
    model(PriorModel): The trained model to be used for evaluation.
    plist(np.array): List of particle PDGs for which the model was trained.
    require_scale(bool): True if a scaling file is provided or else False.
    scale(TemperatureScaling) (optional): Calibration object constructed for temperature scaling.

Definition at line 72 of file evalPriors.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
		list	particlelist,
		str	Model,
		str	prescaling = None
	)

Initialize the Priors class.

Parameters:
    particlelist(list(int)): List of PDG values for which the model was trained.
    Model(str): Path to a previously trained model which will be used to calculate priors.
    prescaling(str) (optional): Path to the scaling file created while training the model.

Definition at line 83 of file evalPriors.py.

    def __init__(self, particlelist: list, Model: str, prescaling: str = None):
        """
        Initialize the Priors class.
 
        Parameters:
            particlelist(list(int)): List of PDG values for which the model was trained.
            Model(str): Path to a previously trained model which will be used to calculate priors.
            prescaling(str) (optional): Path to the scaling file created while training the model.
        """
        model = PriorModel(len(particlelist))
        model.load_state_dict(torch.load(Model))
        model.eval()
        if torch.cuda.is_available():
            model = model.to("cuda")
        
        self.model = model
        if prescaling is not None:
            scale = scaling(prescaling)
            
            self.scale = scale
            
            self.require_scale = True
        else:
            
            self.require_scale = False
        
        self.plist = np.sort(particlelist)
 

Member Function Documentation

calculate_priors()

def calculate_priors	(		self,
		np.array	momentum,
		np.array	cosTheta
	)

Calculates priors for given momentum and cos(theta).

Parameters:
    momentum(np.array): A numpy array containing the momentum of particles.
    cosTheta(np.array): A numpy array containing the cosTheta information of particles.

Returns:
    None.

Definition at line 111 of file evalPriors.py.

get_posterior()

np.array get_posterior	(		self,
		int	pid,
		int	pdg = None
	)

Get PID posterior probabilities.

Parameters:
    pid(np.array): The PID values for the particles used during training process arranged in ascending order of PDG values.
    pdg(int) (optional): PDG value of particle for which posterior is required.

Returns:
    A 1D array of posterior probabilities in case PDG value is provided else returns a 2D array containing
    the posteriors for all particles.

Definition at line 152 of file evalPriors.py.

get_priors()

np.array get_priors	(		self,
		int	pdg = None
	)

Gives the calculated PID priors.

Parameters:
    pdg(int) (optional): The PDG value of the particles for which prior probabilities are needed.

Returns:
    A 1D array conatining prior probabilities for required particle in case PDG value is specified;
    else it will return a 2D array for all particles that were used during training.

Definition at line 135 of file evalPriors.py.

Member Data Documentation

require_scale

require_scale

True if the scaling object exist.

False if the scaling object doesn't exist.

Definition at line 104 of file evalPriors.py.

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

• analysis/scripts/evalPriors.py