Public Member Functions
	__init__ (self, list particlelist, str Model, str prescaling=None)

	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 = model
	The torch model for prior calculation.

	scale = scale
	Temperature scaling object for calibration.

bool	require_scale = True
	True if the scaling object exist.

	plist = np.sort(particlelist)
	False if the scaling object doesn't exist.

	prior = out
	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()

__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()

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.

    def calculate_priors(self, momentum: np.array, cosTheta: np.array):
        """
        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.
        """
        y = data_load(np.hstack((cosTheta.reshape(-1, 1), momentum.reshape(-1, 1))))
        if torch.cuda.is_available():
            y = y.to("cuda")
        out = self.model(y)
        if torch.cuda.is_available():
            out = out.to("cpu")
        out = out.detach().numpy()
 
        if self.require_scale:
            out = self.scale.transform(out)
        
        self.prior = out
 

◆ 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.

    def get_posterior(self, pid: int, pdg: int = None) -> np.array:
        """
        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.
        """
        priorpid = np.multiply(self.prior, pid)
        sumprpid = np.sum(priorpid, axis=1)
        posterior = np.divide(priorpid, sumprpid.reshape(-1, 1))
        if pdg is None:
            return posterior
        else:
            index = np.where(self.plist == pdg)[0][0]
            return posterior[:, index]

◆ 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 containing 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.

    def get_priors(self, pdg: int = None) -> np.array:
        """
        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 containing 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.
        """
        if pdg is not None:
            index = np.where(self.plist == pdg)[0][0]
            return self.prior[:, index]
        else:
            return self.prior
 

Member Data Documentation

◆ model

model = model

The torch model for prior calculation.

Definition at line 98 of file evalPriors.py.

◆ plist

plist = np.sort(particlelist)

False if the scaling object doesn't exist.

Sorted particle PDG list.

Definition at line 109 of file evalPriors.py.

◆ prior

prior = out

Numpy array containing PID prior probability data.

Definition at line 133 of file evalPriors.py.

◆ require_scale

bool require_scale = True

True if the scaling object exist.

Definition at line 104 of file evalPriors.py.

◆ scale

scale = scale

Temperature scaling object for calibration.

Definition at line 102 of file evalPriors.py.

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

analysis/scripts/evalPriors.py

Public Member Functions

Public Attributes