def test_estimate_competence_kuncheva_ex():
query = np.atleast_2d([1, 1])
a_posteriori_test = APosteriori([create_base_classifier(return_value=1)],
k=k_ex_kuncheva)
a_posteriori_test.n_classifiers_ = 1
a_posteriori_test.DSEL_processed_ = dsel_processed_kuncheva
a_posteriori_test.dsel_scores_ = dsel_scores_ex_kuncheva
a_posteriori_test.DSEL_target_ = y_dsel_ex_kuncheva_dependent
a_posteriori_test.n_classes_ = n_classes_ex_kuncheva
neighbors = neighbors_ex_kuncheva.reshape(1, -1)
distances = distances_ex_kuncheva.reshape(1, -1)
predictions = []
for clf in a_posteriori_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
competences = a_posteriori_test.estimate_competence(
query, neighbors, distances, predictions=np.array(predictions))
assert np.isclose(competences, 0.95, atol=0.01)
def test_estimate_competence_diff_target(index, example_all_ones):
_, _, neighbors, distances, dsel_processed, _ = example_all_ones
query = np.atleast_2d([1, 1])
a_posteriori_test = APosteriori()
a_posteriori_test.n_classifiers_ = 3
a_posteriori_test.DSEL_processed_ = dsel_processed
a_posteriori_test.dsel_scores_ = np.ones((15, 3, 3))
a_posteriori_test.DSEL_target_ = np.ones(15, dtype=int) * 2
a_posteriori_test.n_classes_ = 2
neighbors = neighbors[index, :].reshape(1, -1)
distances = distances[index, :].reshape(1, -1)
expected = [0.0, 0.0, 0.0]
predictions = np.array([0, 1, 0])
competences = a_posteriori_test.estimate_competence(query, neighbors,
distances,
predictions=np.array(
predictions))
assert np.isclose(competences, expected).all()
def test_estimate_competence_kuncheva_ex_batch(example_kuncheva):
# considering a batch composed of 10 samples
query = np.ones((10, 2))
a_posteriori_test = APosteriori(k=example_kuncheva['k'])
a_posteriori_test.fit(example_kuncheva['dsel_processed'],
example_kuncheva['y_dependent'])
a_posteriori_test.DSEL_processed_ = example_kuncheva['dsel_processed']
a_posteriori_test.dsel_scores_ = example_kuncheva['dsel_scores']
a_posteriori_test.n_classes_ = example_kuncheva['n_classes']
# repeating the same matrix in a new axis to simulate a batch input.
neighbors = np.tile(example_kuncheva['neighbors'], (10, 1))
distances = np.tile(example_kuncheva['distances'], (10, 1))
predictions = np.ones((1, 10))
competences = a_posteriori_test.estimate_competence(query, neighbors,
distances,
predictions=np.array(
predictions))
assert np.allclose(competences, 0.95, atol=0.01)
def test_estimate_competence_kuncheva_ex_batch():
# considering a batch composed of 10 samples
query = np.ones((10, 2))
a_posteriori_test = APosteriori([create_base_classifier(return_value=1)],
k=k_ex_kuncheva)
a_posteriori_test.fit(dsel_processed_kuncheva,
y_dsel_ex_kuncheva_dependent)
a_posteriori_test.DSEL_processed_ = dsel_processed_kuncheva
a_posteriori_test.dsel_scores_ = dsel_scores_ex_kuncheva
a_posteriori_test.n_classes_ = n_classes_ex_kuncheva
# repeating the same matrix in a new axis to simulate a batch input.
neighbors = np.tile(neighbors_ex_kuncheva, (10, 1))
distances = np.tile(distances_ex_kuncheva, (10, 1))
predictions = []
for clf in a_posteriori_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
competences = a_posteriori_test.estimate_competence(
query, neighbors, distances, predictions=np.array(predictions))
assert np.allclose(competences, 0.95, atol=0.01)
def test_estimate_competence_diff_target(index):
query = np.atleast_2d([1, 1])
pool_classifiers = create_pool_classifiers()
a_posteriori_test = APosteriori(pool_classifiers=pool_classifiers)
a_posteriori_test.n_classifiers_ = len(pool_classifiers)
a_posteriori_test.DSEL_processed_ = dsel_processed_ex1
a_posteriori_test.dsel_scores_ = np.ones((15, 3, 3))
a_posteriori_test.DSEL_target_ = np.ones(15, dtype=int) * 2
a_posteriori_test.n_classes_ = 2
neighbors = neighbors_ex1[index, :].reshape(1, -1)
distances = distances_all_ones[index, :].reshape(1, -1)
expected = [0.0, 0.0, 0.0]
predictions = []
for clf in a_posteriori_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
competences = a_posteriori_test.estimate_competence(query, neighbors,
distances,
predictions=np.array(
predictions))
assert np.isclose(competences, expected).all()
def test_estimate_competence_all_ones(index):
query = np.atleast_2d([1, 1])
a_posteriori_test = APosteriori(create_pool_classifiers())
a_posteriori_test.fit(X_dsel_ex1, y_dsel_ex1)
a_posteriori_test.DSEL_processed_ = dsel_processed_ex1
a_posteriori_test.dsel_scores_ = dsel_scores_all_ones
neighbors = neighbors_ex1[index, :].reshape(1, -1)
distances = distances_all_ones[index, :].reshape(1, -1)
expected = [1.0, 1.0, 1.0]
predictions = []
for clf in a_posteriori_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
competences = a_posteriori_test.estimate_competence(
query, neighbors, distances, predictions=np.array(predictions))
assert np.isclose(competences, expected).all()
def test_estimate_competence_kuncheva_ex_batch(example_kuncheva):
# considering a batch composed of 10 samples
query = np.ones((10, 2))
classifier = MagicMock()
classifier.predict.return_value = [1]
classifier.predict_proba.return_value = None
a_posteriori_test = APosteriori(pool_classifiers=classifier,
k=example_kuncheva['k'])
a_posteriori_test.n_classifiers_ = 1
a_posteriori_test.DSEL_processed_ = example_kuncheva['dsel_processed']
a_posteriori_test.DSEL_target_ = example_kuncheva['y_dependent']
a_posteriori_test.dsel_scores_ = example_kuncheva['dsel_scores']
a_posteriori_test.n_classes_ = example_kuncheva['n_classes']
# repeating the same matrix in a new axis to simulate a batch input.
neighbors = example_kuncheva['neighbors']
distances = example_kuncheva['distances']
predictions = [1]
competences = a_posteriori_test.estimate_competence(
query, neighbors, distances, predictions=np.array(predictions))
assert np.allclose(competences, 0.95, atol=0.01)
def test_fit():
a_posteriori_test = APosteriori(create_pool_classifiers())
a_posteriori_test.fit(X_dsel_ex1, y_dsel_ex1)
assert np.isclose(a_posteriori_test.dsel_scores,
[0.5, 0.5, 1.0, 0.0, 0.33, 0.67]).all()
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
def test_fit():
a_posteriori_test = APosteriori(create_pool_classifiers())
a_posteriori_test.fit(X_dsel_ex1, y_dsel_ex1)
expected = np.array([[0.5, 0.5], [1.0, 0.0], [0.33, 0.67]])
expected = np.tile(expected, (15, 1, 1))
assert np.array_equal(a_posteriori_test.dsel_scores, expected)
X_train, y_train)
model_svc = SVC(probability=True).fit(X_train, y_train)
model_bayes = GaussianNB().fit(X_train, y_train)
model_tree = DecisionTreeClassifier().fit(X_train, y_train)
model_knn = KNeighborsClassifier(n_neighbors=5).fit(X_train, y_train)
pool_classifiers = [
model_perceptron, model_linear_svm, model_svc, model_bayes, model_tree,
model_knn
]
# Initializing the DS techniques
knop = KNOP(pool_classifiers)
rrc = RRC(pool_classifiers)
lca = LCA(pool_classifiers)
mcb = MCB(pool_classifiers)
aposteriori = APosteriori(pool_classifiers)
# Fitting the techniques
knop.fit(X_dsel, y_dsel)
rrc.fit(X_dsel, y_dsel)
lca.fit(X_dsel, y_dsel)
mcb.fit(X_dsel, y_dsel)
aposteriori.fit(X_dsel, y_dsel)
# Calculate classification accuracy of each technique
print('Evaluating DS techniques:')
print('Classification accuracy KNOP: ', knop.score(X_test, y_test))
print('Classification accuracy RRC: ', rrc.score(X_test, y_test))
print('Classification accuracy LCA: ', lca.score(X_test, y_test))
print('Classification accuracy A posteriori: ',
aposteriori.score(X_test, y_test))
def test_check_estimator():
check_estimator(APosteriori(selection_method='best'))
def test_not_predict_proba(create_X_y):
X, y = create_X_y
clf1 = Perceptron()
clf1.fit(X, y)
with pytest.raises(ValueError):
APosteriori([clf1, clf1]).fit(X, y)
random_state=rng)
# Split the data into training and DSEL for DS techniques
X_train, X_dsel, y_train, y_dsel = train_test_split(X_train,
y_train,
test_size=0.5,
random_state=rng)
# Considering a pool composed of 10 base classifiers
pool_classifiers = RandomForestClassifier(n_estimators=10,
random_state=rng,
max_depth=10)
pool_classifiers.fit(X_train, y_train)
# DS techniques without DFP
apriori = APriori(pool_classifiers)
aposteriori = APosteriori(pool_classifiers)
ola = OLA(pool_classifiers)
lca = LCA(pool_classifiers)
desp = DESP(pool_classifiers)
meta = METADES(pool_classifiers)
apriori.fit(X_dsel, y_dsel)
aposteriori.fit(X_dsel, y_dsel)
ola.fit(X_dsel, y_dsel)
lca.fit(X_dsel, y_dsel)
desp.fit(X_dsel, y_dsel)
meta.fit(X_dsel, y_dsel)
print('Evaluating DS techniques:')
print('Classification accuracy of OLA: ', ola.score(X_test, y_test))
print('Classification accuracy of LCA: ', lca.score(X_test, y_test))
