def test_inverse_dual():
nu = 0.3
hm = 0.2e-6
# E*
d = FEMData("E*", [70, 60])
y_pred = np.array(
[
inverse_model_dual(x[0] * 1e9, x[-1] * 1e9, 60, x[2], x[1], nu, hm)[1] / 1e9
for x in d.X
]
)[:, None]
mape = np.mean(np.abs(y_pred - d.y) / d.y) * 100
print("E* MAPE:", mape)
# sigma_y
d = FEMData("sigma_y", [70, 60])
y_pred = np.array(
[
inverse_model_dual(x[0] * 1e9, x[-1] * 1e9, 60, x[2], x[1], nu, hm)[3] / 1e9
for x in d.X
]
)[:, None]
mape = np.mean(np.abs(y_pred - d.y) / d.y) * 100
print("sigma_y MAPE:", mape)
def validation_FEM(yname, angles, train_size):
datafem = FEMData(yname, angles)
# datafem = BerkovichData(yname)
if train_size == 80:
kf = RepeatedKFold(n_splits=5, n_repeats=2, random_state=0)
elif train_size == 90:
kf = KFold(n_splits=10, shuffle=True, random_state=0)
else:
kf = ShuffleSplit(n_splits=10,
test_size=len(datafem.X) - train_size,
random_state=0)
mape = []
iter = 0
for train_index, test_index in kf.split(datafem.X):
iter += 1
print("\nCross-validation iteration: {}".format(iter))
X_train, X_test = datafem.X[train_index], datafem.X[test_index]
y_train, y_test = datafem.y[train_index], datafem.y[test_index]
data = dde.data.DataSet(X_train=X_train,
y_train=y_train,
X_test=X_test,
y_test=y_test)
mape.append(dde.apply(nn, (data, )))
print(mape)
print(yname, train_size, np.mean(mape), np.std(mape))
def validation_exp_cross3(yname):
datalow = FEMData(yname, [70])
dataBerkovich = BerkovichData(yname)
dataexp1 = ExpData("../data/Al6061.csv", yname)
dataexp2 = ExpData("../data/Al7075.csv", yname)
ape = []
y = []
for _ in range(10):
print("\nIteration: {}".format(len(ape)))
data = dde.data.MfDataSet(
X_lo_train=datalow.X,
X_hi_train=np.vstack((dataBerkovich.X, dataexp1.X)),
y_lo_train=datalow.y,
y_hi_train=np.vstack((dataBerkovich.y, dataexp1.y)),
X_hi_test=dataexp2.X,
y_hi_test=dataexp2.y,
)
res = dde.apply(mfnn, (data, ))
ape.append(res[:2])
y.append(res[2])
print(yname)
print(np.mean(ape, axis=0), np.std(ape, axis=0))
np.savetxt("y.dat", np.hstack(y))
def validation_exp_cross2(yname, train_size):
datalow = FEMData(yname, [70])
dataBerkovich = BerkovichData(yname)
dataexp1 = ExpData("../data/B3067.csv", yname)
dataexp2 = ExpData("../data/B3090.csv", yname)
ape = []
y = []
kf = ShuffleSplit(n_splits=10, train_size=train_size, random_state=0)
for train_index, _ in kf.split(dataexp1.X):
print("\nIteration: {}".format(len(ape)))
print(train_index)
data = dde.data.MfDataSet(
X_lo_train=datalow.X,
X_hi_train=np.vstack((dataBerkovich.X, dataexp1.X[train_index])),
y_lo_train=datalow.y,
y_hi_train=np.vstack((dataBerkovich.y, dataexp1.y[train_index])),
X_hi_test=dataexp2.X,
y_hi_test=dataexp2.y,
)
res = dde.apply(mfnn, (data, ))
ape.append(res[:2])
y.append(res[2])
print(yname, train_size)
print(np.mean(ape, axis=0), np.std(ape, axis=0))
np.savetxt(yname + ".dat", np.hstack(y).T)
def validation_exp_cross(yname):
datalow = FEMData(yname, [70])
dataBerkovich = BerkovichData(yname)
dataexp = ExpData("../data/B3067.csv", yname)
train_size = 10
ape = []
y = []
# cases = range(6)
# for train_index in itertools.combinations(cases, 3):
# train_index = list(train_index)
# test_index = list(set(cases) - set(train_index))
kf = ShuffleSplit(n_splits=10,
test_size=len(dataexp.X) - train_size,
random_state=0)
for train_index, test_index in kf.split(dataexp.X):
print("\nIteration: {}".format(len(ape)))
print(train_index, "==>", test_index)
data = dde.data.MfDataSet(
X_lo_train=datalow.X,
X_hi_train=np.vstack((dataBerkovich.X, dataexp.X[train_index])),
y_lo_train=datalow.y,
y_hi_train=np.vstack((dataBerkovich.y, dataexp.y[train_index])),
X_hi_test=dataexp.X[test_index],
y_hi_test=dataexp.y[test_index],
)
res = dde.apply(mfnn, (data, ))
ape.append(res[:2])
y.append(res[2])
print(yname)
print(np.mean(ape, axis=0), np.std(ape, axis=0))
np.savetxt(yname + ".dat", np.hstack(y).T)
def validation_mf(yname, train_size):
datalow = FEMData(yname, [70])
# datalow = ModelData(yname, 10000, "forward_n")
datahigh = BerkovichData(yname)
# datahigh = FEMData(yname, [70])
kf = ShuffleSplit(n_splits=10,
test_size=len(datahigh.X) - train_size,
random_state=0)
# kf = LeaveOneOut()
mape = []
iter = 0
for train_index, test_index in kf.split(datahigh.X):
iter += 1
print("\nCross-validation iteration: {}".format(iter), flush=True)
data = dde.data.MfDataSet(
X_lo_train=datalow.X,
X_hi_train=datahigh.X[train_index],
y_lo_train=datalow.y,
y_hi_train=datahigh.y[train_index],
X_hi_test=datahigh.X[test_index],
y_hi_test=datahigh.y[test_index],
)
mape.append(dde.apply(mfnn, (data, ))[0])
# mape.append(dde.apply(mfgp, (data,)))
print(mape)
print(yname, train_size, np.mean(mape), np.std(mape))
def test_inverse():
nu = 0.3
hm = 0.2e-6
# E*
d = FEMData("E*", [70])
# d = BerkovichData("E*")
# d = ExpData("B3067.csv", "E*")
y_pred = np.array(
[inverse_model(x[0] * 1e9, x[2], x[1], nu, hm)[1] / 1e9 for x in d.X]
)[:, None]
ape = np.abs(y_pred - d.y) / d.y * 100
print("E* APE:", np.mean(ape), np.std(ape))
np.savetxt("E.dat", np.hstack((d.y, y_pred)))
# sigma_y
d = FEMData("sigma_y", [70])
# d = BerkovichData("sigma_y")
# d = ExpData("B3067.csv", "sigma_y")
y_pred = np.array(
[inverse_model(x[0] * 1e9, x[2], x[1], nu, hm)[3] / 1e9 for x in d.X]
)[:, None]
ape = np.abs(y_pred - d.y) / d.y * 100
print("sigma_y APE:", np.mean(ape), np.std(ape))
np.savetxt("sy.dat", np.hstack((d.y, y_pred)))
# n
# d = FEMData("n", [70])
d = BerkovichData("n")
y_pred = np.array([inverse_model(x[0] * 1e9, x[2], x[1], nu, hm)[2] for x in d.X])[
:, None
]
print(d.y)
print(y_pred)
ape = np.abs(y_pred - d.y) / d.y * 100
print("n APE:", np.mean(ape), np.std(ape))
np.savetxt("n.dat", np.hstack((d.y, y_pred)))
def validation_scaling(yname):
datafem = FEMData(yname, [70])
# dataexp = ExpData(yname)
dataexp = BerkovichData(yname, scale_c=True)
mape = []
for iter in range(10):
print("\nIteration: {}".format(iter))
data = dde.data.DataSet(X_train=datafem.X,
y_train=datafem.y,
X_test=dataexp.X,
y_test=dataexp.y)
mape.append(nn(data))
print(yname)
print(np.mean(mape), np.std(mape))
def validation_model(yname, train_size):
datafem = FEMData(yname, [70])
mape = []
for iter in range(10):
print("\nIteration: {}".format(iter))
datamodel = ModelData(yname, train_size, "forward")
X_train, X_test = datamodel.X, datafem.X
y_train, y_test = datamodel.y, datafem.y
data = dde.data.DataSet(X_train=X_train,
y_train=y_train,
X_test=X_test,
y_test=y_test)
# mape.append(svm(data))
mape.append(nn(data))
print(yname, train_size)
print(np.mean(mape), np.std(mape))
def validation_exp(yname):
datalow = FEMData(yname, [70])
dataBerkovich = BerkovichData(yname)
dataexp = ExpData("../data/B3067.csv", yname)
ape = []
y = []
for iter in range(10):
print("\nIteration: {}".format(iter))
data = dde.data.MfDataSet(
X_lo_train=datalow.X,
X_hi_train=dataBerkovich.X,
y_lo_train=datalow.y,
y_hi_train=dataBerkovich.y,
X_hi_test=dataexp.X,
y_hi_test=dataexp.y,
)
res = dde.apply(mfnn, (data, ))
ape.append(res[:2])
y.append(res[2])
print(yname)
print(np.mean(ape, axis=0), np.std(ape, axis=0))
np.savetxt(yname + ".dat", np.hstack(y).T)