def findParameters(folder = "data/dataForMeta/ostanek/"):
"""
V funkciji zaganjamo algoritem MetaDES na diskretnem naboru parametrov. Za vsako iteracijo shranimo zgrajen model,
shranimo rezultat po decilih in shranimo izrisan graf, ki nam zraven rezultata algoritma MetaDes izrise se rezultat
vseh algoritmov, ki so bili uporabljeni v ensemblu MetaDES.
:return:
"""
XMeta, YMeta, XSel, YSel, XTest, YTest = readForMeta2(folder = folder)
nb = GaussianNB()#meta classifier for metaDes
nb.name="Bayes"
rf = RandomForestClassifier(n_estimators=1000, n_jobs=2)
rf.name="rf"
elm = GenELMClassifier(hidden_layer = RandomLayer(n_hidden = 400, activation_func = 'multiquadric', alpha=1))
elm.name="elm"
lr = LogisticRegression()
lr.name= "lr"
metaClassifiers = [lr, elm]
hCs = [1.0, 0.5]
nrNeigh = [305]#, 1000, 3000]
modes = ["weighted"]
metrics = ["l2", "chebyshev"]#BallTree.valid_metrics
metaClsModes = ["combined"]
normalizeMetaFeatures = [True, False]
competenceTressholds = [0.4,0.5,0.6]
# metaDes = MetaDES(0.8,1000, 50, lr, competenceTresshold=0.5, mode="weightedAll")
YCaMeta = readClsResponse("Meta", folder=folder) #we read all classifications for meta dataset
YCaSel = readClsResponse("Sel", folder = folder)
YCaTest = readClsResponse("Test", folder = folder)
nrOfTrials = 0
print("we are testing for %d neighbours" %nrNeigh[0])
allTrials = len(nrNeigh)*len(hCs)*len(modes)*len(metrics)*len(metaClassifiers)*len(metaClsModes)*len(normalizeMetaFeatures)
print("We will have %d trials" %allTrials)
for nrN in nrNeigh:
for hC in hCs:
for mode in modes:
for metric in metrics:
try:
metaDes = MetaDES(hC,nrN, nrN, lr, competenceTresshold=0.5, mode=mode,
metric=metric)
print("calculating meta features...")
metaDes.fit(XMeta, YMeta, YCaMeta, folder = folder)
for cls in metaClassifiers:
for metaClsMode in metaClsModes:
for normalizeMetaFeat in normalizeMetaFeatures:
metaDes.metaClsMode = metaClsMode
metaDes.metaCls = cls
metaDes.normalizeMetaFeat = normalizeMetaFeat
name = "metaDes_hC"+str(metaDes.hC)+\
"_K"+str(metaDes.K)+\
"_Kp"+str(metaDes.Kp)+\
"_mode"+metaDes.mode+\
"_competence"+str(metaDes.competenceTresshold)+\
"_cls"+metaDes.metaCls.name+\
"_metric"+metaDes.metric+\
"_metaClsMode"+metaDes.metaClsMode+\
"_normMetaFeat" + str(metaDes.normalizeMetaFeat)
nrOfTrials += 1
print("Fitting %d/%d trial" %(nrOfTrials,allTrials))
metaDes.fitWithAlreadySaved(saveModel = False, folder = folder) #if we already computed features
responseTest = metaDes.predict_proba(XTest, YCaTest, XSel, YSel, YCaSel)[:,1]
plotClassifiersAndSaveResult(YTest,YCaTest, responseTest, name, folder=folder) #we save figure and save results
Helpers.shraniModel(metaDes,folder+"models/"+name+"/") #we save fitted model
except Exception as e:
allTrials -= 1
with open(folder+"error.log", "a") as fw:
fw.write("We were executing "+name+"\n")
fw.write(str(traceback.format_exc())+"\n\n\n***************************************")
print(str(e))
