def gerar_dataset(self):
print((self.datasets[0]))
ds = ct.dataset2dict(
self.datasets[self.ui.tbDatasets.currentIndex().row()])
dsb.build0(ds)
return
def gerar_ds(self):
'''
self.dataset['path_cll'] = self.dataset['main_path']+'CLL'
self.dataset['path_fl'] = self.dataset['main_path']+'FL'
self.dataset['path_mcl'] = self.dataset['main_path']+'MCL'
'''
ds = ct_datasets.dataset2dict(self.datasets[self.ui.tbDatasets.currentIndex().row()])
dsb.build0(ds)
# dsb.build0(ds)
return
def set_edit(self):
ds = ct.searchRecords(self.idnum)
self.dataset = ct.dataset2dict(ds[0])
self.ui.leIdDataset.setText(self.dataset['id_dataset'])
self.ui.leDescricao.setText(self.dataset['descricao'])
self.ui.leMainPath.setText(self.dataset['main_path'])
index = self.ui.cbEspectro.findText(self.dataset['espectro'])
self.ui.cbEspectro.setCurrentIndex(index)
index = self.ui.cbCanal.findText(self.dataset['canal'])
self.ui.cbCanal.setCurrentIndex(index)
index = self.ui.cbWave.findText(self.dataset['wavelet1'])
self.ui.cbWave.setCurrentIndex(index)
index = self.ui.cbWave2.findText(self.dataset['wavelet2'])
self.ui.cbWave2.setCurrentIndex(index)
index = self.ui.cbWMae.findText(self.dataset['wmae'])
self.ui.cbWMae.setCurrentIndex(index)
index = self.ui.cbN1.findText(self.dataset['nivel'])
self.ui.cbN1.setCurrentIndex(index)
self.ui.leSubbandFile.setText(self.dataset['file_hdf'])
self.ui.leResultFile.setText(self.dataset['file_hdf_result'])
self.ui.leClasse0.setText(self.dataset['target0'])
self.ui.leClasse1.setText(self.dataset['target1'])
self.ui.leClasse2.setText(self.dataset['target2'])
# Chave para o arquivo hdf
self.ui.leIdDataset.setText(self.dataset['id_dataset'])
# Redutores
self.ui.leRedutor1.setText(self.dataset['redutor1'])
self.ui.leRedutor2.setText(self.dataset['redutor2'])
return
def results2(self):
ds = ct_datasets.dataset2dict(self.datasets[self.ui.tbDatasets.currentIndex().row()])
#stores = pd.HDFStore(ds['file_hdf'])
storer = pd.HDFStore(ds['file_hdf_result'])
row = {}
for wavelet in ('DWT2', 'SWT2'):
key = ds['id_dataset']+'/'+wavelet+'/Detalhes/Atributos'
atributos_subbandas = 0
if wavelet=='DWT2':
atributos_subbandas = 362095
elif wavelet=='SWT2':
atributos_subbandas = 1443520
atributos_full = (1388*1040)
for redutor in ('Anova-02', 'Anova-005', 'Anova-05', 'Anova-001', 'Anova-09', 'Full'):
for subbanda in ('Aproximacao', 'Horizontais', 'Verticais', 'Diagonais'):
for kernel in ['linear']:
key = ds['id_dataset']+'/'+wavelet+'/Resultados/'+redutor+'/'+subbanda+'/'
if redutor!='Full':
shape = int(((storer[key+'Shape']).tolist())[1])
row['atributos'] = shape
row['reducao'] = ((shape*1.0)/atributos_subbandas)*100
fator = (storer[key+'Fator'])[0]
else:
row['atributos'] = atributos_full
row['reducao'] = 0.0
fator = 0.0
recall = 0
f1 = 0
precision = 0
accuracy = 0
std = 0
num_fl = 0
num_mcl = 0
num_cll = 0
metricas = (storer[key+kernel+'/metrics2']).as_matrix()
cmtotal = (storer[key+kernel+'/cmtotal']).as_matrix()
recall = metricas[0].mean()
f1 = metricas[1].mean()
precision = metricas[2].mean()
accuracy = metricas[3].mean()
std = metricas[3].std()
row['id_dataset'] = ds['id_dataset']
row['wavelet'] = wavelet
row['wavemae'] = ds['wmae']
row['redutor'] = redutor
row['subbanda'] = subbanda
row['atributos_subbanda'] = atributos_subbandas
row['kernel'] = kernel
row['espectro'] = ds['espectro']
row['canal'] = ds['canal']
row['acuracia'] = accuracy
row['f1'] = f1
row['precisao'] = precision
row['recall'] = recall
row['desvio'] = std
row['fator'] = fator
row['key_kernel'] = key+kernel+'/metrics'
row['time'] = 0
row['num_fl'] = cmtotal[0,0]
row['num_mcl'] = cmtotal[1,1]
row['num_cll'] = cmtotal[2,2]
row['fl_fl'] = cmtotal[0,0]
row['fl_mcl'] = cmtotal[0,1]
row['fl_cll'] = cmtotal[0,2]
row['mcl_fl'] = cmtotal[1,0]
row['mcl_mcl'] = cmtotal[1,1]
row['mcl_cll'] = cmtotal[1,2]
row['cll_fl'] = cmtotal[2,0]
row['cll_mcl'] = cmtotal[2,1]
row['cll_cll'] = cmtotal[2,2]
print(('Salvando: ', key+kernel))
ct_results.addresult(row)
print('Operação realizada com sucesso')
print('Fechando arquivo')
storer.close()
# exit(0)
return
def calc_atributos(self):
ds = ct_datasets.dataset2dict(self.datasets[self.ui.tbDatasets.currentIndex().row()])
atsb.calc_atr_sb(ds)
return
def r_h5_reduct(self):
ds = ct_datasets.dataset2dict(self.datasets[self.ui.tbDatasets.currentIndex().row()])
clfr0(ds)
return
def r_confusao_anova(self):
ds = ct_datasets.dataset2dict(self.datasets[self.ui.tbDatasets.currentIndex().row()])
clfr2.clfr0(ds)
return