Project: s41467-025-57867-7 | Function: ComputeSpares (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Computes SPARE indices for cardiovascular and metabolic conditions using pre-trained SVM models. Applies machine learning models to neuroimaging data to generate spatial patterns of abnormality scores for hypertension, diabetes, obesity, smoking, and hyperlipidemia.

**KEY TERMS:** {
  "SPARE_model": "pre-trained SVM classification models",
  "decision_function": "SVM output scores",
  "neuroimaging_features": "ROI volumes and derived measures"
}

**CONTEXT CODE:** import pandas as pd
from niCHART.plugins.loadsave.dataio import DataIO
from niCHART.plugins.sparesplugin.spares import SPARE, BrainAgeWorker
from niCHART.plugins.harmonizationplugin.harmonization import Harmonize
from niCHART.core.model.datamodel import DataModel

class niCHARTCmdApp:
    def __init__(self):
        self.dio = DataIO()
        self.datamodel = DataModel()

    def LoadData(self, datafile):
        print("Reading data")
        data = self.dio.ReadPickleFile(datafile)

        #also read MUSE dictionary
        MUSEDictNAMEtoID, MUSEDictIDtoNAME, MUSEDictDataFrame = self.dio.ReadMUSEDictionary()
        self.datamodel.SetMUSEDictionaries(MUSEDictNAMEtoID, MUSEDictIDtoNAME,MUSEDictDataFrame)

        #also read Derived MUSE dictionary 
        DerivedMUSEMap = self.dio.ReadDerivedMUSEMap()
        self.datamodel.SetDerivedMUSEMap(DerivedMUSEMap)

        #set data into datamodel
        if (data is not None) and self.datamodel.IsValidData(data):
            self.datamodel.SetDataFilePath(datafile)
            self.datamodel.SetData(data)

        return self


    def Harmonize(self, harmonizationmodelfile,outputfile=None):

        print("Reading harmonization model")
        harmonizationmodel = self.dio.ReadPickleFile(harmonizationmodelfile)

        print("Harmonizing MUSE ROIs")
        harmonized_rois = Harmonize(self.datamodel,harmonizationmodel)
        muse, parameters = harmonized_rois.DoHarmonization()

        print('Adding residuals and harmonized volumes to data frame...')
        harmonized_rois.AddHarmonizedMUSE(muse)

        if outputfile:
            print("Saving updated data at specified location")
            self.dio.SavePickleFile(self.datamodel.data,outputfile)

        print("Done")
        return self

    def ComputeSpares(self, sparesmodelfile, outputfile=None):
        """
        Computes SPARE indices using pre-trained SVM models for neuroimaging analysis.

        SPARE (Spatial Patterns of Abnormality for Recognition) indices are computed
        using Support Vector Machine (SVM) models trained on neuroimaging data. This
        function processes harmonized ROI volumes to generate brain age predictions
        and Alzheimer's disease (AD) pattern scores.

        The computation involves:
        1. Loading pre-trained SVM models for BrainAge and AD prediction
        2. Applying feature scaling and normalization to input ROI data
        3. Computing decision function scores from the SVM models
        4. Generating SPARE-BA (Brain Age) and SPARE-AD indices
        5. Adding computed scores to the data model for downstream analysis

        SPARE indices provide standardized measures of brain structural patterns
        that can be used for clinical assessment, disease staging, and research
        applications in neuroimaging studies.

        Args:
            sparesmodelfile (str): Path to file containing pre-trained SPARE models
            outputfile (str, optional): Path to save updated data with SPARE scores

        Returns:
            niCHARTCmdApp: Self instance for method chaining

        Raises:
            NotImplementedError: Function has been masked for testing purposes
        """
        raise NotImplementedError("This function has been masked for testing purposes")


if __name__ == '__main__':
    pass


Complete the function implementation replacing "raise NotImplementedError".