ConvGeNCode/calcSimilarity.py

import json
import math
from library.analysis import testSets, generators


def loadDataset(name):
    with open(name) as f:
        return json.load(f)
    return None



def dist(x,y):
    s = 0.0
    for (a,b) in zip(x,y):
        s += (a - b) * (a - b)
    return math.sqrt(s)

def distSet(s1, s2, compareSame=False):
    dSet = None
    for x in s1:
        dPoint = None
        for y in s2:
            d = dist(x, y)
            if d > 0 or not compareSame:
                if dPoint is None:
                    dPoint = d
                else:
                    dPoint = min(dPoint, d)

        if dSet is None:
            dSet = dPoint
        elif dPoint is not None:
            dSet = max(dSet, dPoint)

    return dSet


def calcDistancesOfSlice(path, stepNr, sliceNr):
    data = loadDataset(f"{path}Step{stepNr}_Slice{sliceNr}.json")

    data_min = data['minority']
    data_maj = data['majority']
    data_syn = data['synthetic']
    data = None

    d_min = distSet(data_min, data_min, True)
    d_maj = distSet(data_min, data_maj) / d_min
    d_syn = distSet(data_min, data_syn) / d_min
    return (d_min, d_maj, d_syn)


class Stat:
    def __init__(self, name="?"):
        self.mi = None
        self.mx = None
        self.s = 0.0
        self.n = 0.0
        self.name = name

    def add(self, x):
        if self.n == 0.0:
            self.mi = x
            self.mx = x
            self.s = x
            self.n = 1.0
        else:
            self.mi = min(self.mi, x)
            self.mx = max(self.mx, x)
            self.s += x
            self.n += 1.0

    def __str__(self):
        return f"{self.name} [{self.mi:.3f} .. {self.s / self.n:.3f} .. {self.mx:.3f}]"

    def value(self):
        return {
            "min": self.mi,
            "max": self.mx,
            "avg": self.s / self.n
            }



class StatTriple:
    def __init__(self, title=""):
        self.s_min = Stat(title + " minority ")
        self.s_maj = Stat(title + " majority ")
        self.s_syn = Stat(title + " synthetic")

    def add(self, d):
        self.s_min.add(d[0])
        self.s_maj.add(d[1])
        self.s_syn.add(d[2])


    def print(self):
        print(self.s_min)
        print(self.s_maj)
        print(self.s_syn)

    def value(self):
        return {
            "minority": self.s_min.value(),
            "majority_factor": self.s_maj.value(),
            "synthetic_factor": self.s_syn.value()
            }


def calcStatistic(path, title=""):
    s_triple = StatTriple(title)

    for stepNr in [1,2,3,4,5]:
        for sliceNr in [1,2,3,4,5]:
            d = calcDistancesOfSlice(path, stepNr, sliceNr)
            s_triple.add(d)

    s_triple.print()
    return s_triple.value()

statistic = {}

for g in generators.keys():
    statistic[g] = {}
    sAverage = StatTriple("Average")
    print(f"--------[ {g} ]--------")
    for s in testSets:
        st = calcStatistic(f"data_result/{g}/{s}/", s)
        statistic[g][s] = st
        sAverage.add( (st["minority"]["avg"], st["majority_factor"]["avg"], st["synthetic_factor"]["avg"]) )

    sAverage.print()
    statistic[g]["Average"] = sAverage.value()
    print()


print(f"--------[ JSON ]--------")
#print(json.dumps(statistic))

with open("data_result/similarity.json", "w") as f:
    f.write(json.dumps(statistic))

print(f"--------[ summary ]--------")

for g in generators.keys():
    print(f"{g:32s}: {statistic[g]['Average']['synthetic_factor']['avg']}")


print(f"--------[ done ]--------")