def test_predict(self):
reg = KNeighborsRegressor(n_neighbors=1)
reg.fit([[1, 2], [6, 7], [8, 9]], [[1, 0], [20, 5], [8, 6]])
neigh_idx = reg.regressor.predict([[1, 2], [8, 9], [6, 7]])
assert (neigh_idx[0] == [1, 0]).all()
assert (neigh_idx[1] == [8, 6]).all()
assert (neigh_idx[2] == [20, 5]).all()
def test_kneighbors(self):
reg = KNeighborsRegressor(n_neighbors=2)
reg.fit([[1, 2], [6, 7], [8, 9]], [[1, 0], [20, 5], [8, 6]])
neigh_idx = reg.regressor.kneighbors([[6, 6]],
return_distance=False)[0]
assert neigh_idx[0] == 1
assert neigh_idx[1] == 2
assert len(neigh_idx) == 2
def test_wrong2(self):
# wrong number of values
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
category=np.VisibleDeprecationWarning)
with self.assertRaises(Exception):
reg = KNeighborsRegressor()
reg.fit([[1, 2], [
6,
], [8, 9]], [[20, 5], [8, 6]])
def test_with_db_predict(self):
reg = KNeighborsRegressor(n_neighbors=1)
pod = POD()
db = Database(
np.array([1, 2, 3])[:, None],
np.array([1, 5, 3])[:, None])
rom = ReducedOrderModel(db, pod, reg)
rom.fit()
assert rom.predict([1]) == 1
assert rom.predict([2]) == 5
assert rom.predict([3]) == 3
def test_params(self):
reg = KNeighborsRegressor(n_neighbors=20, algorithm='kd_tree')
assert reg.regressor.get_params()['n_neighbors'] == 20
assert reg.regressor.get_params()['algorithm'] == 'kd_tree'
def test_fit_biparam_bifunc(self):
reg = KNeighborsRegressor()
reg.fit([[1, 2], [6, 7], [8, 9]], [[1, 0], [20, 5], [8, 6]])
assert reg.regressor.n_samples_fit_ == 3
def test_fit_biparam_scalarfunc(self):
reg = KNeighborsRegressor()
reg.fit([[1, 2], [6, 7], [8, 9]], [1, 5, 6])
assert reg.regressor.n_samples_fit_ == 3
def test_fit_scalarparam_scalarfunc(self):
reg = KNeighborsRegressor()
reg.fit([1, 2, 5, 7, 2], [2, 5, 7, 83, 3])
assert reg.regressor.n_samples_fit_ == 5
def test_fit_onescalarparam_scalarfunc(self):
reg = KNeighborsRegressor()
reg.fit([1], [20])
assert reg.regressor.n_samples_fit_ == 1
# Moreover, new state-of-the-art methods will arrive, so we invite you to read the [documentation](https://mathlab.github.io/EZyRB/) for the complete list of all the possibilities!
#
# In the next cell, we create two dictionaries with the objects, such that we can easily test everything with simple `for` cycles. **WARNING** since several methods require the solution of an optimization problem (eg. GPR, ANN, AE), the cell may require some minutes to be run.
# In[9]:
reductions = {
'POD': POD('svd', rank=10),
'AE': AE([200, 100, 10], [10, 100, 200], nn.Tanh(), nn.Tanh(), 10),
}
approximations = {
# 'Linear': Linear(),
'RBF': RBF(),
'GPR': GPR(),
'KNeighbors': KNeighborsRegressor(),
'RadiusNeighbors': RadiusNeighborsRegressor(),
'ANN': ANN([20, 20], nn.Tanh(), 10),
}
header = '{:10s}'.format('')
for name in approximations:
header += ' {:>15s}'.format(name)
print(header)
for redname, redclass in reductions.items():
row = '{:10s}'.format(redname)
for approxname, approxclass in approximations.items():
rom = ROM(db, redclass, approxclass)
rom.fit()
row += ' {:15e}'.format(rom.kfold_cv_error(n_splits=5).mean())