def test_joblib_pickle(self):
reg1 = self.regressor_class(**self.kwargs)
reg1.fit(self.X_train, self.y_train)
y_pred1 = reg1.predict(self.X_test)
joblib.dump(reg1, 'test_reg.pkl')
# Remove model file
cleanup()
reg2 = joblib.load('test_reg.pkl')
y_pred2 = reg2.predict(self.X_test)
np.testing.assert_allclose(y_pred1, y_pred2)
def test_pickle(self):
reg1 = self.regressor_class(**self.kwargs)
reg1.fit(self.X_train, self.y_train)
y_pred1 = reg1.predict(self.X_test)
s = pickle.dumps(reg1)
# Remove model file
cleanup()
reg2 = pickle.loads(s)
y_pred2 = reg2.predict(self.X_test)
np.testing.assert_allclose(y_pred1, y_pred2)
def test_joblib_pickle(self):
clf1 = self.classifier_class(**self.kwargs)
clf1.fit(self.X_train, self.y_train)
y_pred1 = clf1.predict(self.X_test)
joblib.dump(clf1, 'test_clf.pkl')
# Remove model file
cleanup()
clf2 = joblib.load('test_clf.pkl')
y_pred2 = clf2.predict(self.X_test)
np.testing.assert_allclose(y_pred1, y_pred2)
def test_pickle(self):
clf1 = self.classifier_class(**self.kwargs)
clf1.fit(self.X_train, self.y_train)
y_pred1 = clf1.predict(self.X_test)
s = pickle.dumps(clf1)
# Remove model file
cleanup()
clf2 = pickle.loads(s)
y_pred2 = clf2.predict(self.X_test)
np.testing.assert_allclose(y_pred1, y_pred2)
def test_joblib_pickle(self):
est1 = self.estimator_class(**self.kwargs)
est1.fit(self.X_train, self.y_train)
y_pred1 = est1.predict(self.X_test)
joblib.dump(est1, 'test_est.pkl')
# Remove model file
cleanup()
est2 = joblib.load('test_est.pkl')
y_pred2 = est2.predict(self.X_test)
np.testing.assert_allclose(y_pred1, y_pred2)
def test_pickle(self):
est1 = self.estimator_class(**self.kwargs)
est1.fit(self.X_train, self.y_train)
y_pred1 = est1.predict(self.X_test)
s = pickle.dumps(est1)
# Remove model file
cleanup()
est2 = pickle.loads(s)
y_pred2 = est2.predict(self.X_test)
np.testing.assert_allclose(y_pred1, y_pred2)