FittedGroupedDEDXEstimatorTrainer Class Reference

Inheritance diagram for FittedGroupedDEDXEstimatorTrainer:

Public Member Functions
def	__init__ (self, result_function, use_sigma_for_result_fitting)
def	create_result_dataframe (self)
def	fit_result_parameters (self)
def	train (self, data)
def	plot_fit_result (self, data)
def	plot_grouped_result (self, data)

Public Attributes
	result_function
	cached copy of the result function
	result_parameters_for_each_dedx_bin
	cached copy of the dictionary of fitting parameters for each dE/dx bin
	use_sigma_for_result_fitting
	cached copy of the flag to add mean+/-sigma values to the output Dataframe
	dedx_estimator_function
	cached copies of the fit parameters and estimator function

Detailed Description

Train a neural network for dE/dx-based particle identification

Definition at line 92 of file train.py.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			result_function,
			use_sigma_for_result_fitting
	)

Constructor

Reimplemented from DEDXEstimationTrainer.

Reimplemented in GaussianEstimatorTrainer, LandauEstimatorTrainer, MaximumEstimatorTrainer, MedianEstimatorTrainer, GaussianEstimatorTrainerSQRT, LandauEstimatorTrainerSQRT, MaximumEstimatorTrainerSQRT, MedianEstimatorTrainerSQRT, and FunctionFittedGroupedDEDXEstimatorTrainer.

Definition at line 95 of file train.py.

    def __init__(self, result_function, use_sigma_for_result_fitting):
        """Constructor"""
 
        
        self.result_function = result_function
        
        self.result_parameters_for_each_dedx_bin = {}
        
        self.use_sigma_for_result_fitting = use_sigma_for_result_fitting
 
        GroupedDEDXEstimationTrainer.__init__(self)
 

Member Function Documentation

create_result_dataframe()

def create_result_dataframe

(

self

)

Fit for the mean dE/dx and standard deviation, return the fit Dataframe

Definition at line 107 of file train.py.

    def create_result_dataframe(self):
        """Fit for the mean dE/dx and standard deviation, return the fit Dataframe"""
        result_df = pd.DataFrame([{"dedx_bin_center": dedx_bin_center,
                                   "mu": fit_parameters[1][1],
                                   "sigma": fit_parameters[0]} for dedx_bin_center,
                                  fit_parameters in self.result_parameters_for_each_dedx_bin.items()
                                  if fit_parameters is not None])
 
        if len(result_df) == 0:
            raise ValueError("Could not find any fitted parameters!")
 
        if self.use_sigma_for_result_fitting:
            result_df["mu_plus_sigma"] = result_df.mu + result_df.sigma
            result_df["mu_minus_sigma"] = result_df.mu - result_df.sigma
 
        result_df.sort("dedx_bin_center", inplace=True)
 
        return result_df
 

fit_result_parameters()

def fit_result_parameters

(

self

)

Define the parameters for the fit, assign initial guesses

Definition at line 126 of file train.py.

    def fit_result_parameters(self):
        """Define the parameters for the fit, assign initial guesses"""
        result_df = self.create_result_dataframe()
 
        p0 = (7e+08, -4e+04, 0.1, 0)
 
        if self.use_sigma_for_result_fitting:
            popt, pcov = curve_fit(self.result_function, result_df.dedx_bin_center, result_df.mu, p0=p0,
                                   sigma=result_df.sigma, absolute_sigma=True)
        else:
            popt, pcov = curve_fit(self.result_function, result_df.dedx_bin_center, result_df.mu, p0=p0)
 
        return popt, lambda dedx: self.result_function(dedx, *popt)
 

plot_fit_result()

def plot_fit_result	(		self,
			data
	)

Plot the fitted results

Definition at line 154 of file train.py.

    def plot_fit_result(self, data):
        """Plot the fitted results"""
        plot_dedx_data = np.linspace(data[self.dedx_column].min(), data[self.dedx_column].max(), 100)
        result_df = self.create_result_dataframe()
 
        plt.plot(plot_dedx_data, self.dedx_estimator_function(plot_dedx_data), color="black", label="Fitted estimator")
        if self.use_sigma_for_result_fitting:
            # color = "black"
            plt.errorbar(result_df.dedx_bin_center, result_df.mu, marker="o", ls="", label="Data Points", yerr=result_df.sigma)
 
        plt.ylim(0, 0.14)
        plt.xlabel("dEdX in ADC count/cm")
        plt.ylabel("p in GeV/c")
        plt.legend(frameon=True)
 

plot_grouped_result()

def plot_grouped_result	(		self,
			data
	)

Plot the fitted grouped results

Reimplemented in FunctionFittedGroupedDEDXEstimatorTrainer.

Definition at line 169 of file train.py.

    def plot_grouped_result(self, data):
        """Plot the fitted grouped results"""
        dedx_binned_data, dedx_bins = self.create_dedx_bins(data)
 
        # List to prevent bug in pd.DataFrame.apply
        already_plotted_list = []
 
        def plot_fitted_results(dedx_bin):
            dedx_bin_center = dedx_bin.mean().values[0]
 
            if dedx_bin_center not in already_plotted_list:
                already_plotted_list.append(dedx_bin_center)
 
                fit_data = self.create_fit_data(dedx_bin)
                plt.plot(fit_data.p_bin_centers, fit_data.number_of_p_values, ls="", marker=".", color="black")
 
            return True
 
        plt.xlabel("p in GeV/c")
        plt.ylabel("Entries")
 
        dedx_binned_data.apply(plot_fitted_results)
 
 

train()

def train	(		self,
			data
	)

Train the neural network using curated data

Reimplemented from DEDXEstimationTrainer.

Definition at line 140 of file train.py.

    def train(self, data):
        """Train the neural network using curated data"""
        dedx_binned_data, dedx_bins = self.create_dedx_bins(data)
 
        def fit_and_save_results(dedx_bin):
            fit_result = self.fit_p_to_dedx_bin(dedx_bin)
            return {dedx_bin.mean()[self.dedx_column]: fit_result}
 
        for result in dedx_binned_data.apply(fit_and_save_results):
            self.result_parameters_for_each_dedx_bin.update(result)
 
        
        self.dedx_estimator_parameters, self.dedx_estimator_function = self.fit_result_parameters()
 

Member Data Documentation

dedx_estimator_function

dedx_estimator_function

cached copies of the fit parameters and estimator function

Definition at line 152 of file train.py.

result_function

result_function

cached copy of the result function

Definition at line 99 of file train.py.

result_parameters_for_each_dedx_bin

result_parameters_for_each_dedx_bin

cached copy of the dictionary of fitting parameters for each dE/dx bin

Definition at line 101 of file train.py.

use_sigma_for_result_fitting

use_sigma_for_result_fitting

cached copy of the flag to add mean+/-sigma values to the output Dataframe

Definition at line 103 of file train.py.

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

• tracking/studies/low_momentum_vxd_estimation/train.py