def synergyCP(X, y, n_source=3):
listIndSrcErrRate = [] # empty list
listSCPErrRate = [] # empty list
listIndSrcEff = [] # empty list
listSCPEff = [] # empty list
listIndSrcVal = [] # empty list
listSCPVal = [] # empty list
listIndSrcOF = [] # empty list
listSCPOF = [] # empty list
for i in range(1):
X_train, X_test, y_train, y_test \
= train_test_split(X, y, test_size=0.2)
X_train, X_calib, y_train, y_calib \
= train_test_split(X_train, y_train, test_size=0.3)
nrTrainCases = len(y_train)
n_labels = len(np.unique(y_train))
randIndex = random.sample(list(range(0, nrTrainCases)), nrTrainCases)
splitLen = int(nrTrainCases / n_source)
# split training data into equal parts
trainIndex = randIndex[0:splitLen]
meanCalibPredProb = np.zeros((len(y_calib), n_labels))
meanTestPredProb = np.zeros((len(y_test), n_labels))
for indexSrc in range(0, n_source):
sourceData = X_train[trainIndex, :]
sourceTarget = y_train[trainIndex]
calibPredProb, testPredProb = icp.ICPClassification(sourceData, sourceTarget, X_calib,\
X_test, method = "rf", nrTrees = 10)
if indexSrc == 0:
srcMCListConfScores = icp.computeConformityScores(
calibPredProb, y_calib)
pValues = icp.computePValues(srcMCListConfScores, testPredProb)
errRate, eff, val, obsFuzz = pm.pValues2PerfMetrics(
pValues, y_test)
listIndSrcVal.append(val)
listIndSrcErrRate.append(errRate)
listIndSrcEff.append(eff)
listIndSrcOF.append(obsFuzz)
pm.CalibrationPlot(pValues, y_test, color='orange')
meanCalibPredProb = np.add(meanCalibPredProb, calibPredProb)
meanTestPredProb = np.add(meanTestPredProb, testPredProb)
trainIndex = randIndex[splitLen * (indexSrc + 1):splitLen *
(indexSrc + 2)]
meanCalibPredProb = meanCalibPredProb / n_source
meanTestPredProb = meanTestPredProb / n_source
srcMCListConfScores = icp.computeConformityScores(
meanCalibPredProb, y_calib)
pValues = icp.computePValues(srcMCListConfScores, meanTestPredProb)
errRate, eff, val, obsFuzz = pm.pValues2PerfMetrics(pValues, y_test)
pm.CalibrationPlot(pValues, y_test, color='b')
print(val)
def ACP(X, y, n_source=3):
listACPOF = [] # empty list
x = PrettyTable()
x.field_names = [
"Validity", "Efficiency", "ErrorRate", "Observed Fuzziness"
]
n_labels = len(np.unique(y))
for i in range(1):
XX, X_test, yy, y_test \
= train_test_split(X, y, test_size=0.2)
meanPValues = np.zeros((len(y_test), n_labels))
#sss = StratifiedShuffleSplit(n_splits=n_source, test_size=1/n_source)
sss = ShuffleSplit(n_splits=n_source, test_size=1 / n_source)
for train_index, test_index in sss.split(XX, yy):
# print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_calib = XX[train_index], XX[test_index]
y_train, y_calib = yy[train_index], yy[test_index]
calibPredProb, testPredProb = icp.ICPClassification(X_train,
y_train,
X_calib,
X_test,
method="rf",
nrTrees=10)
srcMCListConfScores = icp.computeConformityScores(
calibPredProb, y_calib)
pValues = icp.computePValues(srcMCListConfScores, testPredProb)
meanPValues = np.add(meanPValues, pValues)
meanPValues = meanPValues / n_source
errRate, eff, val, obsFuzz = pm.pValues2PerfMetrics(
meanPValues, y_test)
pm.CalibrationPlot(meanPValues, y_test, color='b')
listACPOF.append(obsFuzz)
print(val)
return listACPOF
