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final implementation of an save/load option for the surrogates with p…
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Original file line number | Diff line number | Diff line change |
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@@ -1,51 +1,98 @@ | ||
import os | ||
import unittest | ||
import numpy as np | ||
from smt.surrogate_models import KRG | ||
# from smt.surrogate_models import IDW //TODO comprendre pourquoi l"import de IDW génère une erreur | ||
from smt.surrogate_models import LS | ||
from smt.surrogate_models import KPLS | ||
from smt.surrogate_models import GEKPLS | ||
from smt.surrogate_models import GPX | ||
from smt.surrogate_models import KPLSK | ||
from smt.surrogate_models import MGP | ||
from smt.surrogate_models import QP | ||
# from smt.surrogate_models import RBF | ||
# from smt.surrogate_models import RMTS | ||
from smt.surrogate_models import SGP | ||
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from smt.problems import Sphere | ||
from smt.sampling_methods import LHS | ||
from smt.surrogate_models import KRG, LS, KPLS, GEKPLS, KPLSK, MGP, QP, SGP, GENN, QP | ||
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class TestSaveLoad(unittest.TestCase): | ||
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def test_save_load(self): | ||
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filename = "kriging_save_test" | ||
def test_save_load_GEKPLS(self): | ||
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xt = np.array([0.0, 1.0, 2.0, 3.0, 4.0]) | ||
yt = np.array([0.0, 1.0, 1.5, 0.9, 1.0]) | ||
filename = "sm_save_test" | ||
fun = Sphere(ndim=2) | ||
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sampling = LHS(xlimits=fun.xlimits, criterion="m") | ||
xt = sampling(20) | ||
yt = fun(xt) | ||
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for i in range(2): | ||
yd = fun(xt, kx=i) | ||
yt = np.concatenate((yt, yd), axis=1) | ||
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sm = SGP() | ||
sm.set_training_values(xt, yt) | ||
sm = GEKPLS() | ||
sm.set_training_values(xt, yt[:, 0]) | ||
for i in range(2): | ||
sm.set_training_derivatives(xt, yt[:, 1 + i].reshape((yt.shape[0], 1)), i) | ||
sm.train() | ||
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sm._save(filename) | ||
sm.save(filename) | ||
self.assertTrue(os.path.exists(filename), f"Le fichier {filename} n'a pas été créé.") | ||
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file_size = os.path.getsize(filename) | ||
print(f"Taille du fichier : {file_size} octets") | ||
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sm2 = sm._load(filename) | ||
sm2 = GEKPLS.load(filename) | ||
self.assertIsNotNone(sm2) | ||
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num = 100 | ||
x = np.linspace(0.0, 4.0, num).reshape(-1, 1) | ||
X = np.arange(fun.xlimits[0, 0], fun.xlimits[0, 1], 0.25) | ||
Y = np.arange(fun.xlimits[1, 0], fun.xlimits[1, 1], 0.25) | ||
X, Y = np.meshgrid(X, Y) | ||
Z = np.zeros((X.shape[0], X.shape[1])) | ||
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y = sm2.predict_values(x) | ||
for i in range(X.shape[0]): | ||
for j in range(X.shape[1]): | ||
Z[i, j] = sm.predict_values( | ||
np.hstack((X[i, j], Y[i, j])).reshape((1, 2)) | ||
).item() | ||
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self.assertIsNotNone(y) | ||
print("Prédictions avec le modèle chargé :", y) | ||
self.assertIsNotNone(Z) | ||
print("Prédictions avec le modèle chargé :", Z) | ||
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os.remove(filename) | ||
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def test_save_krigs(self): | ||
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krigs = [KRG, KPLS, KPLSK, MGP, SGP, QP, GENN, LS] | ||
rng = np.random.RandomState(1) | ||
N_inducing = 30 | ||
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xt = np.array([0.0, 1.0, 2.0, 3.0, 4.0]) | ||
yt = np.array([0.0, 1.0, 1.5, 0.9, 1.0]) | ||
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for surrogate in krigs: | ||
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filename = "sm_save_test" | ||
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sm = surrogate() | ||
sm.set_training_values(xt, yt) | ||
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if surrogate == SGP: | ||
sm.Z = 2 * rng.rand(N_inducing, 1) - 1 | ||
sm.set_inducing_inputs(Z=sm.Z) | ||
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sm.train() | ||
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sm.save(filename) | ||
self.assertTrue(os.path.exists(filename), f"Le fichier {filename} n'a pas été créé.") | ||
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file_size = os.path.getsize(filename) | ||
print(f"Taille du fichier : {file_size} octets") | ||
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sm2 = surrogate.load(filename) | ||
self.assertIsNotNone(sm2) | ||
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num = 100 | ||
x = np.linspace(0.0, 4.0, num).reshape(-1, 1) | ||
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y = sm2.predict_values(x) | ||
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self.assertIsNotNone(y) | ||
print("Prédictions avec le modèle chargé :", y) | ||
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os.remove(filename) | ||
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if __name__ == "__main__": | ||
unittest.main() |
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Original file line number | Diff line number | Diff line change |
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@@ -0,0 +1,16 @@ | ||
import pickle | ||
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def save(self, filename): | ||
try: | ||
with open(filename, 'wb') as file: | ||
pickle.dump(self, file) | ||
print("model saved") | ||
except: | ||
print("Couldn't save the model") | ||
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@staticmethod | ||
def load(filename): | ||
with open(filename, "rb") as file: | ||
sm = pickle.load(file) | ||
return sm |