compteur = 0
proba = pm.Bernoulli('p',0.5)
best_val = 0
kf = KFold(len(X_trainDF),5,shuffle=True,random_state=55)
while compteur < Iteration:
print compteur
C = 10**(uniform(-6,-2))
p = uniform(3,6)
npca = randrange(5, 30)
which_feature = {k:int(proba.random()) for k in Feature.transformer_weights.keys()}
which_feature['HOGFeature'] = 1
which_feature['SobelFeature'] = 1
Feature.transformer_weights = which_feature
param = {'SobelFeature__PCA__n_components':npca,
'RawImage__PCA__n_components':npca,
'HOGFeature__PCA__n_components':npca}
Feature.set_params(**param)
scores = []; rocauctr = []; rocaucval = []
print 'Debut cross-validation'
for train_index, val_index in kf:
X_trDF, X_valDF = X_trainDF.iloc[train_index], X_trainDF.iloc[val_index]
y_trDF, y_valDF = y_trainDF.iloc[train_index], y_trainDF.iloc[val_index]
X_tr = Feature.fit_transform(X_trDF)
y_tr = np.array(y_trDF)[:,np.newaxis]
X_val = Feature.transform(X_valDF)
