for train_index, test_index in skf.split(atribute, output):
x_train_com, x_test = atribute.iloc[train_index, :], atribute.iloc[
test_index, :]
y_train_com, Y_test = output.iloc[train_index, :], output.iloc[
test_index, :]
x_train_un, x_train_st, y_train_un, Y_train = train_test_split(
x_train_com, y_train_com, test_size=testsize2, random_state=31)
""" STRUCTURED PREDICTORS """
ACC_ST[i, :], HL_ST[i, :], time_ST[i, :] = Strukturni(
x_train_com, y_train_com, x_test, Y_test)
""" UNSTRUCTURED PREDICTORS """
Skor_com_AUC[i, :], Skor_com_AUC2[i, :], Skor_com_ACC[i, :], Skor_com_ACC2[
i, :], Skor_com_HL[
i, :], R_train, R_test, R2, Noinst_train, Noinst_test, timeUN[
i, :] = Nestrukturni_fun(x_train_un, y_train_un, x_train_st,
Y_train, x_test, Y_test, No_class)
""" STructured matrix """
Se_train, Se_test = Struktura_fun(No_class, NoGraph, R2, y_train_com,
Noinst_train, Noinst_test)
""" Model GCRFC """
Y_train = Y_train.values
Y_test = Y_test.values
start_time = time.time()
mod1 = GCRFCNB()
mod1.fit(R_train,
Se_train,
Y_train,
learn='TNC',
learnrate=6e-4,
maxiter=iteracija)
staze = np.load('staze.npy')
staze = staze.astype(int)
skijasi = pd.read_csv(str(staze[0]),index_col='date1')
output = skijasi.label
atribute = skijasi.drop(['label','vreme_pros'],axis=1)
skf = KFold(n_splits = broj_fold)
skf.get_n_splits(atribute, output)
i = 0
for train_index,test_index in skf.split(atribute, output):
timeST[i,:], Skor_R2_struct[i,:] = Strukturni_predict_fun(train_index, test_index, ModelSTNo)
Skor_com_AUC[i,:], Skor_com_AUC2[i,:], R_train, R_test, R2, Noinst_train, Noinst_test, Y_train, Y_test = Nestrukturni_fun(train_index, test_index, No_class, testsize2)
Se_train, Se_test = Struktura_fun(No_class,NoGraph,R2, train_index, test_index, Noinst_train, Noinst_test, testsize2)
""" Model GCRFC """
start_time = time.time()
mod1 = GCRFCNB()
mod1.fit(R_train, Se_train, Y_train, learn = 'TNC', learnrate = 6e-4, maxiter = iteracija)
probNB, YNB = mod1.predict(R_test,Se_test)
timeNB[i] = time.time() - start_time
start_time = time.time()
mod2 = GCRFC()
mod2.fit(R_train, Se_train, Y_train, learn = 'TNC', learnrate = 3e-4, learnratec = 0.5, maxiter = iteracija)
np.save('mod2',mod2.x)
test_index, :]
x_train_un, x_train_st, y_train_un1, Y_train1 = train_test_split(
x_train_com, y_train_com, test_size=testsize2, random_state=31)
Y_test = Y_test1.iloc[:, :18]
Y_train = Y_train1.iloc[:, :18]
y_train_un = y_train_un1.iloc[:, :18]
Y_test_reg = Y_test1.iloc[:, 18:]
Y_train_reg = Y_train1.iloc[:, 18:]
y_train_un_reg = y_train_un1.iloc[:, 18:]
timeST[i, :], Skor_R2_struct[i, :] = Strukturni_predict_fun(
train_index, test_index, atribute, output, ModelSTNo)
Skor_com_AUC[i, :], Skor_com_AUC2[
i, :], R_train, R_test, R2, Noinst_train, Noinst_test = Nestrukturni_fun(
x_train_un, y_train_un, x_train_st, Y_train, x_test, Y_test,
No_class)
Se_train, Se_test = Struktura_fun(No_class, NoGraph, R2, y_train_com,
Noinst_train, Noinst_test)
""" Model GCRFC """
Y_train = Y_train.values
Y_test = Y_test.values
start_time = time.time()
mod1 = GCRFCNB()
mod1.fit(R_train,
Se_train,
Y_train,
learn='TNC',
learnrate=6e-4,
maxiter=iteracija)
y_train_com, Y_test = output.iloc[train_index,:], output.iloc[test_index,:]
provera = Y_test[Y_test==1].any().all()
print(provera)
file = open("rezultatiEMOCIJE.txt","w")
for train_index,test_index in skf.split(atribute, output):
x_train_com, x_test = atribute.iloc[train_index,:], atribute.iloc[test_index,:]
y_train_com, Y_test = output.iloc[train_index,:], output.iloc[test_index,:]
x_train_un, x_train_st, y_train_un, Y_train = train_test_split(x_train_com, y_train_com, test_size=testsize2, random_state=31)
""" STRUCTURED PREDICTORS """
ACC_ST[i,:], HL_ST[i,:], time_ST[i,:] = Strukturni(x_train_com, y_train_com, x_test, Y_test)
""" UNSTRUCTURED PREDICTORS """
Skor_com_AUC[i,:], Skor_com_AUC2[i,:], Skor_com_ACC[i,:], Skor_com_ACC2[i,:], Skor_com_HL[i,:], R_train, R_test, R2, Noinst_train, Noinst_test, timeUN[i,:] = Nestrukturni_fun(x_train_un, y_train_un, x_train_st, Y_train, x_test, Y_test, No_class)
""" STructured matrix """
Se_train, Se_test = Struktura_fun(No_class,NoGraph, R2 , y_train_com, Noinst_train, Noinst_test)
""" Model GCRFC """
Y_train = Y_train.values
Y_test = Y_test.values
start_time = time.time()
mod1 = GCRFCNB()
mod1.fit(R_train, Se_train, Y_train, learn = 'TNC', learnrate = 6e-4, maxiter = iteracija)
probNB, YNB = mod1.predict(R_test,Se_test)
timeNB[i] = time.time() - start_time
start_time = time.time()