def test_BRKnna_predict_dense(self):
data = csr.csr_matrix([[0, 1], [1, 1], [1, 1.1], [0.5, 1]])
train_ids = [['lid0', 'lid1'], ['lid2', 'lid3'], ['lid4', 'lid3'], ['lid4', 'lid5']]
mlb = MultiLabelBinarizer()
y = mlb.fit_transform(train_ids)
knn = BRKNeighborsClassifier(threshold=0.5, n_neighbors=3, mode='a')
knn.fit(data, y)
pred = knn.predict(csr.csr_matrix([[1.1, 1.1]])).todense()
np.testing.assert_array_equal([[0, 0, 0, 1, 1, 0]], pred)
def test_BRKnnb_predict_two_samples(self):
data = csr.csr_matrix([[0, 1], [1, 1.1], [1, 1], [0.5, 1]])
train_ids = [['lid0', 'lid1'], ['lid0', 'lid1'], ['lid4', 'lid5'], ['lid4', 'lid5']]
mlb = MultiLabelBinarizer(sparse_output=True)
y = mlb.fit_transform(train_ids)
knn = BRKNeighborsClassifier(mode='b', n_neighbors=3)
knn.fit(data, y)
pred = knn.predict(csr.csr_matrix([[0, 1], [2, 2]])).todense()
np.testing.assert_array_equal([[1, 1, 0, 0], [0, 0, 1, 1]], pred)
def test_BRKnna_no_labels_take_closest(self):
data = csr.csr_matrix([[0, 1], [1, 1], [1, 1.1], [0, 1]])
train_ids = [['lid0', 'lid1'], ['lid2', 'lid3'], ['lid2', 'lid3'], ['lid0', 'lid5']]
mlb = MultiLabelBinarizer(sparse_output=True)
y = mlb.fit_transform(train_ids)
knn = BRKNeighborsClassifier(n_neighbors=2, threshold=0.6, mode='a')
knn.fit(data, y)
pred = knn.predict(csr.csr_matrix([[0, 1]])).todense()
print(pred)
np.testing.assert_array_equal([[1, 0, 0, 0, 0]], pred)
def test_BRKnnb_auto_optimize_k(self):
data = csr.csr_matrix([[0, 1], [1, 1], [0, 1.1], [1.1, 1]])
train_ids = [['lid0', 'lid1'], ['lid0', 'lid1'], ['lid2', 'lid3'], ['lid0', 'lid1']]
mlb = MultiLabelBinarizer()
y = mlb.fit_transform(train_ids)
knn = BRKNeighborsClassifier(mode='b', n_neighbor_candidates=[1, 3], auto_optimize_k=True)
# noinspection PyUnusedLocal
def fun(s, X, y_):
return data[[1, 2, 3]], data[[0]], y[[1, 2, 3]], y[[0]]
BRKNeighborsClassifier._get_split = fun
knn.fit(data, y)
self.assertEquals(3, knn.n_neighbors)
pred = knn.predict(csr.csr_matrix([[0.1, 1], [2, 2]])).todense()
np.testing.assert_array_equal([[1, 1, 0, 0], [1, 1, 0, 0]], pred)