def test_affinity_with_queryable_knn_index(self):
knn_index = nearest_neighbors.Sklearn(self.x, k=30)
init = initialization.random(self.x, random_state=0)
for aff in [
affinity.PerplexityBasedNN(knn_index=knn_index, perplexity=30),
affinity.Uniform(knn_index=knn_index, k_neighbors=30),
affinity.FixedSigmaNN(knn_index=knn_index, sigma=1),
affinity.Multiscale(knn_index=knn_index, perplexities=[10,
20]),
affinity.MultiscaleMixture(knn_index=knn_index,
perplexities=[10, 20]),
]:
# Without initilization
embedding = TSNE().fit(affinities=aff)
self.eval_embedding(embedding, self.y, aff.__class__.__name__)
new_embedding = embedding.prepare_partial(self.x)
new_embedding.optimize(50, learning_rate=1, inplace=True)
self.eval_embedding(new_embedding, self.y,
f"transform::{aff.__class__.__name__}")
# With initilization
embedding = TSNE().fit(affinities=aff, initialization=init)
self.eval_embedding(embedding, self.y, aff.__class__.__name__)
new_embedding = embedding.prepare_partial(self.x)
new_embedding.optimize(50, learning_rate=1, inplace=True)
self.eval_embedding(new_embedding, self.y,
f"transform::{aff.__class__.__name__}")
def test_accepts_knn_index(self):
knn_index = nearest_neighbors.Sklearn(self.iris, k=30)
for method_name, cls in AFFINITY_CLASSES:
aff = cls(knn_index=knn_index)
self.assertIs(aff.knn_index, knn_index, msg=method_name)
self.assertEqual(aff.n_samples, self.iris.shape[0])
def test_to_new(self):
knn_index = nearest_neighbors.Sklearn(self.iris, k=30)
for method_name, cls in AFFINITY_CLASSES:
aff = cls(knn_index=knn_index)
aff.to_new(self.iris)
def test_fails_if_both_data_and_index_specified(self):
knn_index = nearest_neighbors.Sklearn(self.iris, k=30)
for method_name, cls in AFFINITY_CLASSES:
with self.assertRaises(ValueError, msg=method_name):
cls(data=self.iris, knn_index=knn_index)