def test_affinity(self):
init = initialization.random(self.x, random_state=0)
for aff in [
affinity.PerplexityBasedNN(self.x, perplexity=30),
affinity.Uniform(self.x, k_neighbors=30),
affinity.FixedSigmaNN(self.x, sigma=1, k=30),
affinity.Multiscale(self.x, perplexities=[10, 30]),
affinity.MultiscaleMixture(self.x, perplexities=[10, 30]),
]:
# 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(10, learning_rate=0.1, 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(10, learning_rate=0.1, inplace=True)
self.eval_embedding(new_embedding, self.y,
f"transform::{aff.__class__.__name__}")
def test_affinity_with_precomputed_neighbors(self):
nn = NearestNeighbors(n_neighbors=30)
nn.fit(self.x)
distances, neighbors = nn.kneighbors(n_neighbors=30)
knn_index = nearest_neighbors.PrecomputedNeighbors(
neighbors, distances)
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__)
# With initilization
embedding = TSNE().fit(affinities=aff, initialization=init)
self.eval_embedding(embedding, self.y, aff.__class__.__name__)
def test_2(self):
init = initialization.pca(self.x)
aff = affinity.MultiscaleMixture(self.x, perplexities=[5, 30])
embedding = openTSNE.TSNEEmbedding(init, aff)
embedding.optimize(25, exaggeration=12, momentum=0.5, inplace=True)
embedding.optimize(50, exaggeration=1, momentum=0.8, inplace=True)
self.eval_embedding(embedding, self.y)
new_embedding = embedding.transform(self.x)
self.eval_embedding(new_embedding, self.y, "transform")
def test_affinity_with_precomputed_distances(self):
d = squareform(pdist(self.x))
knn_index = nearest_neighbors.PrecomputedDistanceMatrix(d, 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, aff.__class__.__name__)
# With initilization
embedding = TSNE().fit(affinities=aff, initialization=init)
self.eval_embedding(embedding, aff.__class__.__name__)