topology/noisyTerrain/GS/noisyTerrain_eval.py

import vtk
import numpy as np
import gudhi
import sys
import os

# Add the topology directory to Python path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../..'))

###############################################################################
# The following parameters are from `topologyScoring.py` 
###############################################################################
# Set to True to allow data that is not perfectly predicted to score a perfect 10.
# If this is set to False, the highest possible score that an imperfect prediction can score is a 9.
canImperfectPredictionsScore10 = False

# The order of the Wasserstein distance
wassersteinOrder = 1.0

# The ground metric used for computing the Wasserstein distance
wassersteinGroundMetric = float('inf')

# This is the maximum average Wasserstein distance (the average is taken over (|P|+|Q|)/2) that can score points.
# Any distance above this score will score a 0.
maximumAverageWassersteinDistance = 0.2

###############################################################################
# You can integrate the following two functions into `topologyScoring.py` 
############################################################################### 
def _loadPersistenceDiagramFromVTK(pdFilename : str) -> np.ndarray:
    """
    Load a persistence diagram from a VTK file computed with TTK.
    
    Args:
        pdFilename: The path to the VTK file containing the persistence diagram.
        
    Returns:
        A numpy array of shape (n, 2) where each row is a (birth, death) pair for finite persistence pairs.
    """
    reader = vtk.vtkDataSetReader()
    reader.SetFileName(pdFilename)
    reader.Update()
    
    output = reader.GetOutput()
    if output is None:
        raise ValueError(f"Could not read VTK file: {pdFilename}")
    
    cellData = output.GetCellData()
    
    birthArray = cellData.GetArray("Birth")
    persistenceArray = cellData.GetArray("Persistence")
    isFiniteArray = cellData.GetArray("IsFinite")
    
    if birthArray is None or persistenceArray is None:
        raise ValueError(f"VTK file {pdFilename} does not contain required 'Birth' and 'Persistence' arrays")
    
    pairs = []
    numCells = output.GetNumberOfCells()
    
    for i in range(numCells):
        isFinite = isFiniteArray.GetTuple1(i) if isFiniteArray else 1
        if isFinite:
            birth = birthArray.GetTuple1(i)
            persistence = persistenceArray.GetTuple1(i)
            death = birth + persistence
            pairs.append((birth, death))
    
    return np.array(pairs)


# ====== PERSISTENCE DIAGRAM WASSERSTEIN SCORE ======

def persistenceDiagramWassersteinScore(gtFilename : str, reconFilename : str, verbose : bool = False) -> int:
    """
    Compute a similarity score (0-10) between two persistence diagrams stored in VTK files using Wasserstein distance.
    
    Args:
        gtFilename: Path to the ground truth persistence diagram VTK file.
        reconFilename: Path to the reconstructed persistence diagram VTK file.
        verbose: Whether to print error messages.
        
    Returns:
        An integer score from 0-10 indicating similarity (10 is best).
    """
    try:
        gtDiagram = _loadPersistenceDiagramFromVTK(gtFilename)
    except Exception as e:
        if verbose:
            print(f"Error loading GT diagram: {e}")
        return 0
    
    try:
        reconDiagram = _loadPersistenceDiagramFromVTK(reconFilename)
    except Exception as e:
        if verbose:
            print(f"Error loading recon diagram: {e}")
        return 0
    
    if len(gtDiagram) == 0 and len(reconDiagram) == 0:
        return 10
    elif len(gtDiagram) == 0 or len(reconDiagram) == 0:
        return 0
    
    # Normalize using GT's min-max
    minFunctionValue = np.min(gtDiagram)
    maxFunctionValue = np.max(gtDiagram)
    
    gtDiagram = (gtDiagram - minFunctionValue) / (maxFunctionValue - minFunctionValue)
    reconDiagram = (reconDiagram - minFunctionValue) / (maxFunctionValue - minFunctionValue)
    
    wassersteinDistance = gudhi.wasserstein.wasserstein_distance(gtDiagram, reconDiagram, order=wassersteinOrder, internal_p=wassersteinGroundMetric)
    
    numAverage = (gtDiagram.shape[0] + reconDiagram.shape[0]) / 2
    wassersteinDistance /= numAverage
    
    if wassersteinDistance == 0:
        return 10
    
    score = round(10 * (maximumAverageWassersteinDistance - wassersteinDistance) / maximumAverageWassersteinDistance)
    
    if not canImperfectPredictionsScore10 and score == 10:
        return 9
    
    if score < 0:
        return 0
    
    return score


def evaluateNoisyTerrainPersistenceDiagram(gtFilename : str, reconFilename : str, verbose : bool = False):
    """
    Given two persistence diagrams, return a similarity score from 0-10.
    
    A score of 0 is considered bad and a score of 10 is considered good.

    Args:
        gtFilename: The name of a file in legacy VTK format (.vtk) that stores the persistence diagram of the ground truth data.
        reconFilename: The name of a file in legacy VTK format (.vtk) that stores the persistence diagram of the reconstructed data.
        verbose: Should error messages be printed if there are issues with the input files.
    """
    return persistenceDiagramWassersteinScore(gtFilename, reconFilename, verbose)

if __name__ == "__main__":

    if len(sys.argv) != 3:
        print(f"{os.path.basename(__file__)}: usage is 'python3 {os.path.basename(__file__)} gt_points.vtk recon_points.vtk'")
        exit(1)

    score = evaluateNoisyTerrainPersistenceDiagram(sys.argv[1], sys.argv[2], verbose=True)

    print(f"These critical points scored: {score}")