def test_estimate_competence(index, expected):
ola_test = OLA(create_pool_classifiers())
ola_test.processed_dsel = dsel_processed_ex1
ola_test.neighbors = neighbors_ex1[index, :]
ola_test.distances = distances_ex1[index, :]
ola_test.DFP_mask = [1, 1, 1]
query = np.array([1, 1])
competences = ola_test.estimate_competence(query.reshape(1, -1))
assert np.isclose(competences, expected).all()
def test_estimate_competence_batch(example_estimate_competence):
_, _, neighbors, distances, dsel_processed, _ = example_estimate_competence
expected = np.array([[0.57142857, 0.71428571, 0.71428571],
[0.71428571, 0.85714286, 0.71428571],
[0.57142857, 0.71428571, 0.57142857]])
ola_test = OLA()
ola_test.DSEL_processed_ = dsel_processed
ola_test.DFP_mask = np.ones((3, 3))
competences = ola_test.estimate_competence(neighbors, distances=distances)
assert np.allclose(competences, expected)
def test_estimate_competence_batch():
expected = np.array([[0.57142857, 0.71428571, 0.71428571],
[0.71428571, 0.85714286, 0.71428571],
[0.57142857, 0.71428571, 0.57142857]])
ola_test = OLA(create_pool_classifiers())
ola_test.DSEL_processed_ = dsel_processed_ex1
neighbors = neighbors_ex1
distances = distances_ex1
ola_test.DFP_mask = np.ones((3, 3))
query = np.array([[1, 1], [1, 1], [1, 1]])
competences = ola_test.estimate_competence(query, neighbors, distances=distances)
assert np.allclose(competences, expected)
def _generate_local_pool(self, query):
"""
Local pool generation.
This procedure populates the "pool_classifiers" based on the query sample's neighborhood.
Thus, for each query sample, a different pool is created.
In each iteration, the training samples near the query sample are singled out and a
subpool is generated using the Self-Generating Hyperplanes (SGH) method.
Then, the DCS technique selects the best classifier in the generated subpool and it is added to the local pool.
In the following iteration, the neighborhood is increased and another SGH-generated subpool is obtained
over the new neighborhood, and again the DCS technique singles out the best in it, which is then added to the local pool.
This process is repeated until the pool reaches "n_classifiers".
Parameters
----------
query : array of shape = [n_features]
The test sample.
Returns
-------
self
References
----------
M. A. Souza, G. D. Cavalcanti, R. M. Cruz, R. Sabourin, On the characterization of the
oracle for dynamic classi
er selection, in: International Joint Conference on Neural Networks,
IEEE, 2017, pp. 332-339.
"""
n_samples, _ = self.DSEL_data.shape
self.pool_classifiers = []
n_err = 0
max_err = 2 * self.n_classifiers
curr_k = self.k
# Classifier count
n = 0
while n < self.n_classifiers and n_err < max_err:
subpool = SGH()
included_samples = np.zeros((n_samples), int)
if self.knne:
idx_neighb = np.array([], dtype=int)
# Obtain neighbors of each class individually
for j in np.arange(0, self.n_classes):
# Obtain neighbors from the classes in the RoC
if np.any(self.classes[j] == self.DSEL_target[
self.neighbors[0][np.arange(0, curr_k)]]):
nc = np.where(self.classes[j] == self.DSEL_target[
self.neighbors[0]])
idx_nc = self.neighbors[0][nc]
idx_nc = idx_nc[np.arange(
0, np.minimum(curr_k, len(idx_nc)))]
idx_neighb = np.concatenate((idx_neighb, idx_nc),
axis=0)
else:
idx_neighb = np.asarray(self.neighbors)[0][np.arange(
0, curr_k)]
# Indicate participating instances in the training of the subpool
included_samples[idx_neighb] = 1
curr_classes = np.unique(self.DSEL_target[idx_neighb])
# If there are +1 classes in the local region
if len(curr_classes) > 1:
# Obtain SGH pool
subpool.fit(self.DSEL_data, self.DSEL_target, included_samples)
# Adjust chosen DCS technique parameters
if self.ds_tech == 'ola':
ds = OLA(subpool, k=len(idx_neighb)) # change for self.k
elif self.ds_tech == 'lca':
ds = LCA(subpool, k=len(idx_neighb))
elif self.ds_tech == 'mcb':
ds = MCB(subpool, k=len(idx_neighb))
elif self.ds_tech == 'mla':
ds = MLA(subpool, k=len(idx_neighb))
elif self.ds_tech == 'a_priori':
ds = APriori(subpool, k=len(idx_neighb))
elif self.ds_tech == 'a_posteriori':
ds = APosteriori(subpool, k=len(idx_neighb))
# Fit ds technique
ds.fit(self.DSEL_data, self.DSEL_target)
neighb = np.in1d(
self.neighbors,
idx_neighb) # True/False vector of selected neighbors
# Set distances and neighbors of the query sample (already calculated)
ds.distances = np.asarray([self.distances[0][neighb]
]) # Neighborhood
ds.neighbors = np.asarray([self.neighbors[0][neighb]
]) # Neighborhood
ds.DFP_mask = np.ones(ds.n_classifiers)
# Estimate competence
comp = ds.estimate_competence(query, ds._predict_base(query))
# Select best classifier in subpool
sel_c = ds.select(comp)
# Add to local pool
self.pool_classifiers.append(copy.deepcopy(subpool[sel_c[0]]))
n += 1
# else:
# # Exception: fewer than 2 classes in the neighborhood
# print('OPS! Next!')
# Increase neighborhood size
curr_k += 2
n_err += 1
return self
