trainFacetFilter.py

#!/usr/bin/env python3
 

 
# This is for training a mva classifier for hit triplets
# It performs a tip better than the current default chi2 filter
# However run time is quite a bit slower which is why it won't be used in the standard chain.
# Nevertheless this script can be used to generate variables to consider
# for improvements or cross checks with --task explore
 
import sys
 
from tracking.run.event_generation import StandardEventGenerationRun
from trackfindingcdc.run.training import TrainingRunMixin
 
 
class FacetFilterTrainingRun(TrainingRunMixin, StandardEventGenerationRun):
    """Run for recording facets encountered at the filter"""
    ## Suggested number for this analysis
    n_events = 100
 
    ## Suggested generator module
    generator_module = "generic"
    # detector_setup = "TrackingDetector"
 
    ## Default task set to explore
    task = "explore"
 
    ## Name of the truth variables
    truth = "truth_positive"
 
    ## Option whether to reestimate the drift length
    flight_time_reestimation = False
 
    ## Option whether to use the least square fit to the hit triplet
    facet_least_square_fit = False
 
    @property
    def identifier(self):
        """Database identifier of the filter being trained"""
        return "trackfindingcdc_FacetFilter.xml"
 
    def create_argument_parser(self, **kwds):
        """Convert command-line arguments to basf2 argument list"""
 
        argument_parser = super().create_argument_parser(**kwds)
 
        argument_parser.add_argument(
            "-fr",
            "--flight-time-reestimation",
            action="store_true",
            dest="flight_time_reestimation",
            help="Switch to reestimate drift length before fitting."
        )
 
        argument_parser.add_argument(
            "-fl",
            "--facet-least-square-fit",
            action="store_true",
            dest="facet_least_square_fit",
            help="Switch to fit the facet with least square method for the drift length update"
        )
 
        return argument_parser
 
    def create_path(self):
        """
        Sets up a path that plays back pregenerated events or generates events
        based on the properties in the base class.
        """
 
        path = super().create_path()
 
        ## Post-process events according to the user's desired task (train, eval, explore)
        if self.task == "train":
            var_sets = [
                "mva",
                "filter(truth)",
            ]
 
        elif self.task == "eval":
            var_sets = [
                "filter(chi2)",
                "filter(realistic)",
                "filter(mva)",
                "filter(truth)",
            ]
 
        elif self.task == "explore":
            var_sets = [
                "basic",
                "truth",
                "bend",
                "fit",
                "filter(truth)",
                "filter(realistic)",
                "filter(chi2)",
            ]
 
            ## Signal some variables to select in the classification analysis
            self.variables = [
                "curv",
                "curv_pull",
                "middle_phi_pull",
                "middle_chi2",
                "fit_0_phi0_sigma",
                "chi2_0",
                "chi2_0_per_s",
                "fit_1_phi0_sigma",
                "chi2_1",
                "chi2_1_per_s",
                "fit_phi0_sigma",
                "chi2 chi2_per_s",
                "realistic_accept",
                "chi2_accept",
            ]
 
        path.add_module("TFCDC_WireHitPreparer",
                        flightTimeEstimation="outwards",
                        UseNLoops=1.0)
 
        path.add_module("TFCDC_ClusterPreparer")
 
        path.add_module("TFCDC_SegmentFinderFacetAutomaton",
                        FacetUpdateDriftLength=self.flight_time_reestimation,
                        FacetLeastSquareFit=self.facet_least_square_fit,
                        FacetFilter="unionrecording",
                        FacetFilterParameters={
                            "rootFileName": self.sample_file_name,
                            "varSets": var_sets,
                        },
                        FacetRelationFilter="none")
 
        return path
 
 
def main():
    """Execute the facet recording"""
    run = FacetFilterTrainingRun()
    run.configure_and_execute_from_commandline()
 
 
if __name__ == "__main__":
    import logging
    logging.basicConfig(stream=sys.stdout, level=logging.INFO, format="%(levelname)s:%(message)s")
    main()