def make_prediction(self, couple_test, value_of_neg_class):
print('ST make_prediction')
if self.type_ST == "MolView":
list_train_samples, list_train_labels = self.make_ST_train_set(
self.dico_target_of_mol[couple_test[1]][0],
self.dico_target_of_mol[couple_test[1]][1], [couple_test[0]],
self.K_prot, self.dico_prot2indice, self.threshold_prot,
value_of_neg_class)
K_train, K_test = self.make_Ktrain_and_Ktest_ST(
list_train_samples, [couple_test[0]], self.K_prot,
self.dico_prot2indice)
Y_test_score = []
for param in range(len(self.list_param)):
if self.type_clf == "SVM":
clf = svm.SVC(kernel='precomputed',
C=self.list_param[param],
class_weight='balanced')
clf.fit(K_train, list_train_labels)
Y_test_score.append(clf.decision_function(K_test).tolist())
elif self.type_clf == "KernelRidge":
clf = KernelRidge(alpha=self.list_param[param],
kernel='precomputed')
clf.fit(K_train, list_train_labels)
Y_test_score.append(clf.predict(K_test).tolist())
elif self.type_ST == "Kron":
list_train_samples = []
list_train_labels = []
list_train_samples_mol, list_train_labels_mol = self.make_ST_train_set(
self.dico_target_of_mol[couple_test[1]][0],
self.dico_target_of_mol[couple_test[1]][1], [couple_test[0]],
self.K_prot, self.dico_prot2indice, self.threshold_prot,
value_of_neg_class)
list_train_samples_prot, list_train_labels_prot = self.make_ST_train_set(
self.dico_ligand_of_prot[couple_test[0]][0],
self.dico_ligand_of_prot[couple_test[0]][1], [couple_test[1]],
self.K_mol, self.dico_mol2indice, self.threshold_mol,
value_of_neg_class)
for el in list_train_samples_mol:
list_train_samples.append((el, couple_test[1]))
for el in list_train_samples_prot:
list_train_samples.append((couple_test[0], el))
list_train_labels = list_train_labels_mol + list_train_labels_prot
list_train_samples, list_train_labels = self.update_with_extra_task_pairs(
couple_test, list_train_samples, list_train_labels)
K_train, K_test = self.make_Ktrain_and_Ktest_MT(
list_train_samples, [couple_test])
Y_test_score = []
for param in range(len(self.list_param)):
if self.type_clf == "SVM":
clf = svm.SVC(kernel='precomputed',
C=self.list_param[param],
class_weight='balanced')
clf.fit(K_train, list_train_labels)
Y_test_score.append(clf.decision_function(K_test).tolist())
elif self.type_clf == "KernelRidge":
clf = KernelRidge(alpha=self.list_param[param],
kernel='precomputed')
clf.fit(K_train, list_train_labels)
Y_test_score.append(clf.predict(K_test).tolist())
return Y_test_score
def run(self, ind_sampling, ind_fold, type_calcul, ind_fold_nested):
if self.fold_setting=="S4":
nb_fold = self.nb_fold * self.nb_fold
self.load_CV_indexes(ind_sampling)
if self.CV_type == 'ClusterCV_':
ajout = self.CV_type
else:
ajout = 'CV_'
if type_calcul=="Outer":
K_train, K_test = self.make_Ktrain_and_Ktest_MT(self.samples_tr[ind_fold], self.samples_te[ind_fold])
pred_score = []
for param in range(len(self.list_param)):
if self.type_clf=="SVM":
clf = svm.SVC(kernel='precomputed', C=self.list_param[param])
clf.fit(K_train, self.labels_tr[ind_fold])
Y_test_score = clf.decision_function(K_test).tolist()
elif self.type_clf=="KernelRidge":
clf = KernelRidge(alpha=self.list_param[param], kernel='precomputed')
clf.fit(K_train, inner_labels_tr[ind_fold])
Y_test_score = clf.predict(K_test).tolist()
else:
raise ValueError('invalid value of type_clf')
pred_score.append(Y_test_score)
del clf
del Y_test_score
pickle.dump(pred_score, open('saved_results/MT/MT_Nested'+str(self.nb_fold)+'fold'+ajout+self.fold_setting+"_"+self.type_clf+'_Nested_'+str(ind_fold)+'_'+str(ind_sampling)+".data", 'wb'))
del K_train
del K_test
elif type_calcul=="Inner":
inner_samples_tr = []
inner_labels_tr = []
inner_samples_te = []
inner_labels_te = []
for ind_t in range(self.nb_fold):
if ind_t!=ind_fold_nested:
inner_samples_te.append(self.samples_te[ind_t])
inner_labels_te.append(self.labels_te[ind_t])
for ind_temp in range(self.nb_fold-1):
list_temp_sample = []
list_temp_label = []
for ind_temp_bis in range(self.nb_fold-1):
if ind_temp!=ind_temp_bis:
list_temp_sample += inner_samples_te[ind_temp_bis]
list_temp_label += inner_labels_te[ind_temp_bis]
inner_samples_tr.append(list_temp_sample)
inner_labels_tr.append(list_temp_label)
K_train, K_test = self.make_Ktrain_and_Ktest_MT(inner_samples_tr[ind_fold_nested], inner_samples_te[ind_fold_nested])
pred_score = []
for param in range(len(self.list_param)):
if self.type_clf=="SVM":
clf = svm.SVC(kernel='precomputed', C=self.list_param[param])
clf.fit(K_train, inner_labels_tr[ind_fold_nested])
Y_test_score = clf.decision_function(K_test).tolist()
elif self.type_clf=="KernelRidge":
clf = KernelRidge(alpha=self.list_param[param], kernel='precomputed')
clf.fit(K_train, inner_labels_tr[ind_fold_nested])
Y_test_score = clf.predict(K_test).tolist()
else:
raise ValueError('invalid value of type_clf')
pred_score.append(Y_test_score)
del clf
del Y_test_score
pickle.dump(pred_score, open('saved_results/MT/MT_Nested'+str(self.nb_fold)+'fold'+ajout+self.fold_setting+"_"+self.type_clf+'_In_'+str(ind_fold)+'_'+str(ind_fold_nested)+'_'+str(ind_sampling)+".data", 'wb'))
del K_train
del K_test
