basf2_mva_util.py

 
 
import tempfile
import numpy as np
 
from basf2 import B2WARNING
import basf2_mva
 
 
def chain2dict(chain, tree_columns, dict_columns=None):
    """
    Convert a ROOT.TChain into a dictionary of np.arrays
    @param chain the ROOT.TChain
    @param tree_columns the column (or branch) names in the tree
    @param dict_columns the corresponding column names in the dictionary
    """
    if len(tree_columns) == 0:
        return dict()
    if dict_columns is None:
        dict_columns = tree_columns
    try:
        from ROOT import RDataFrame
        rdf = RDataFrame(chain)
        d = np.column_stack(list(rdf.AsNumpy(tree_columns).values()))
        d = np.core.records.fromarrays(d.transpose(), names=dict_columns)
    except ImportError:
        d = {column: np.zeros((chain.GetEntries(),)) for column in dict_columns}
        for iEvent, event in enumerate(chain):
            for dict_column, tree_column in zip(dict_columns, tree_columns):
                d[dict_column][iEvent] = getattr(event, tree_column)
    return d
 
 
def calculate_roc_auc(p, t):
    """
    Deprecated name of ``calculate_auc_efficiency_vs_purity``
 
    @param p np.array filled with the probability output of a classifier
    @param t np.array filled with the target (0 or 1)
    """
    B2WARNING(
        "\033[93mcalculate_roc_auc\033[00m has been deprecated and will be removed in future.\n"
        "This change has been made as calculate_roc_auc returned the area under the efficiency-purity curve\n"
        "not the efficiency-background retention curve as expected by users.\n"
        "Please replace calculate_roc_auc with:\n\n"
        "\033[96mcalculate_auc_efficiency_vs_purity(probability, target[, weight])\033[00m:"
        " the current definition of calculate_roc_auc\n"
        "\033[96mcalculate_auc_efficiency_vs_background_retention(probability, target[, weight])\033[00m:"
        " what is commonly known as roc auc\n")
    return calculate_auc_efficiency_vs_purity(p, t)
 
 
def calculate_auc_efficiency_vs_purity(p, t, w=None):
    """
    Calculates the area under the efficiency-purity curve
    @param p np.array filled with the probability output of a classifier
    @param t np.array filled with the target (0 or 1)
    @param w None or np.array filled with weights
    """
    if w is None:
        w = np.ones(t.shape)
 
    wt = w * t
 
    N = np.sum(w)
    T = np.sum(wt)
 
    index = np.argsort(p)
    efficiency = (T - np.cumsum(wt[index])) / float(T)
    purity = (T - np.cumsum(wt[index])) / (N - np.cumsum(w[index]))
    purity = np.where(np.isnan(purity), 0, purity)
    return np.abs(np.trapz(purity, efficiency))
 
 
def calculate_auc_efficiency_vs_background_retention(p, t, w=None):
    """
    Calculates the area under the efficiency-background_retention curve (AUC ROC)
    @param p np.array filled with the probability output of a classifier
    @param t np.array filled with the target (0 or 1)
    @param w None or np.array filled with weights
    """
    if w is None:
        w = np.ones(t.shape)
 
    wt = w * t
 
    N = np.sum(w)
    T = np.sum(wt)
 
    index = np.argsort(p)
    efficiency = (T - np.cumsum(wt[index])) / float(T)
    background_retention = (N - T - np.cumsum((np.abs(1 - t) * w)[index])) / float(N - T)
    return np.abs(np.trapz(efficiency, background_retention))
 
 
def calculate_flatness(f, p, w=None):
    """
    Calculates the flatness of a feature under cuts on a signal probability
    @param f the feature values
    @param p the probability values
    @param w optional weights
    @return the mean standard deviation between the local and global cut selection efficiency
    """
    quantiles = list(range(101))
    binning_feature = np.unique(np.percentile(f, q=quantiles))
    binning_probability = np.unique(np.percentile(p, q=quantiles))
    if len(binning_feature) < 2:
        binning_feature = np.array([np.min(f) - 1, np.max(f) + 1])
    if len(binning_probability) < 2:
        binning_probability = np.array([np.min(p) - 1, np.max(p) + 1])
    hist_n, _ = np.histogramdd(np.c_[p, f],
                               bins=[binning_probability, binning_feature],
                               weights=w)
    hist_inc = hist_n.sum(axis=1)
    hist_inc /= hist_inc.sum(axis=0)
    hist_n /= hist_n.sum(axis=0)
    hist_n = hist_n.cumsum(axis=0)
    hist_inc = hist_inc.cumsum(axis=0)
    diff = (hist_n.T - hist_inc)**2
    return np.sqrt(diff.sum() / (100 * 99))
 
 
class Method:
    """
    Wrapper class providing an interface to the method stored under the given identifier.
    It loads the Options, can apply the expert and train new ones using the current as a prototype.
    This class is used by the basf_mva_evaluation tools
    """
 
    def __init__(self, identifier):
        """
        Load a method stored under the given identifier
        @param identifier identifying the method
        """
        # Always avoid the top-level 'import ROOT'.
        import ROOT  # noqa
        # Initialize all the available interfaces
        ROOT.Belle2.MVA.AbstractInterface.initSupportedInterfaces()
        
        self.identifier = identifier
        
        self.weightfile = ROOT.Belle2.MVA.Weightfile.load(self.identifier)
        
        self.general_options = basf2_mva.GeneralOptions()
        self.general_options.load(self.weightfile.getXMLTree())
 
        # This piece of code should be correct but leads to random segmentation faults
        # inside python, llvm or pyroot, therefore we use the more dirty code below
        # Ideas why this is happening:
        # 1. Ownership of the unique_ptr returned by getOptions()
        # 2. Some kind of object slicing, although pyroot identifies the correct type
        # 3. Bug in pyroot
        # interfaces = ROOT.Belle2.MVA.AbstractInterface.getSupportedInterfaces()
        # self.interface = interfaces[self.general_options.m_method]
        # self.specific_options = self.interface.getOptions()
 
        
        self.specific_options = None
        if self.general_options.m_method == "FastBDT":
            self.specific_options = basf2_mva.FastBDTOptions()
        elif self.general_options.m_method == "TMVAClassification":
            self.specific_options = basf2_mva.TMVAOptionsClassification()
        elif self.general_options.m_method == "TMVARegression":
            self.specific_options = basf2_mva.TMVAOptionsRegression()
        elif self.general_options.m_method == "FANN":
            self.specific_options = basf2_mva.FANNOptions()
        elif self.general_options.m_method == "Python":
            self.specific_options = basf2_mva.PythonOptions()
        elif self.general_options.m_method == "PDF":
            self.specific_options = basf2_mva.PDFOptions()
        elif self.general_options.m_method == "Combination":
            self.specific_options = basf2_mva.CombinationOptions()
        elif self.general_options.m_method == "Reweighter":
            self.specific_options = basf2_mva.ReweighterOptions()
        elif self.general_options.m_method == "Trivial":
            self.specific_options = basf2_mva.TrivialOptions()
        else:
            raise RuntimeError("Unknown method " + self.general_options.m_method)
 
        self.specific_options.load(self.weightfile.getXMLTree())
 
        variables = [str(v) for v in self.general_options.m_variables]
        importances = self.weightfile.getFeatureImportance()
 
        
        self.importances = {k: importances[k] for k in variables}
        
        self.variables = list(sorted(variables, key=lambda v: self.importances.get(v, 0.0)))
        
        self.root_variables = [ROOT.Belle2.MakeROOTCompatible.makeROOTCompatible(v) for v in self.variables]
        
        self.root_importances = {k: importances[k] for k in self.root_variables}
        
        self.description = str(basf2_mva.info(self.identifier))
        
        self.spectators = [str(v) for v in self.general_options.m_spectators]
        
        self.root_spectators = [ROOT.Belle2.MakeROOTCompatible.makeROOTCompatible(v) for v in self.spectators]
 
    def train_teacher(self, datafiles, treename, general_options=None, specific_options=None):
        """
        Train a new method using this method as a prototype
        @param datafiles the training datafiles
        @param treename the name of the tree containing the training data
        @param general_options general options given to basf2_mva.teacher
          (if None the options of this method are used)
        @param specific_options specific options given to basf2_mva.teacher
          (if None the options of this method are used)
        """
        # Always avoid the top-level 'import ROOT'.
        import ROOT  # noqa
        if isinstance(datafiles, str):
            datafiles = [datafiles]
        if general_options is None:
            general_options = self.general_options
        if specific_options is None:
            specific_options = self.specific_options
 
        with tempfile.TemporaryDirectory() as tempdir:
            identifier = tempdir + "/weightfile.xml"
 
            general_options.m_datafiles = basf2_mva.vector(*datafiles)
            general_options.m_identifier = identifier
 
            basf2_mva.teacher(general_options, specific_options)
 
            method = Method(identifier)
        return method
 
    def apply_expert(self, datafiles, treename):
        """
        Apply the expert of the method to data and return the calculated probability and the target
        @param datafiles the datafiles
        @param treename the name of the tree containing the data
        """
        import ROOT  # noqa
        if isinstance(datafiles, str):
            datafiles = [datafiles]
        with tempfile.TemporaryDirectory() as tempdir:
            identifier = tempdir + "/weightfile.xml"
            ROOT.Belle2.MVA.Weightfile.save(self.weightfile, identifier)
 
            rootfilename = tempdir + '/expert.root'
            basf2_mva.expert(basf2_mva.vector(identifier),
                             basf2_mva.vector(*datafiles),
                             treename,
                             rootfilename)
            chain = ROOT.TChain("variables")
            chain.Add(rootfilename)
 
            expert_target = identifier + '_' + self.general_options.m_target_variable
            stripped_expert_target = self.identifier + '_' + self.general_options.m_target_variable
 
            output_names = [self.identifier]
            branch_names = [
                    ROOT.Belle2.MakeROOTCompatible.makeROOTCompatible(identifier),
                    ]
            if self.general_options.m_nClasses > 2:
                output_names = [self.identifier+f'_{i}' for i in range(self.general_options.m_nClasses)]
                branch_names = [
                    ROOT.Belle2.MakeROOTCompatible.makeROOTCompatible(
                        identifier +
                        f'_{i}') for i in range(
                        self.general_options.m_nClasses)]
 
            d = chain2dict(
                chain,
                [*branch_names, ROOT.Belle2.MakeROOTCompatible.makeROOTCompatible(expert_target)],
                [*output_names, stripped_expert_target])
 
        return (d[self.identifier] if self.general_options.m_nClasses <= 2 else np.array([d[x]
                for x in output_names]).T), d[stripped_expert_target]