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 tsneScatter(associations,
perplexity,
weigthMin=0,
random=0,
openTsne=True):
if isinstance(associations, nx.Graph):
matrix = nx.to_numpy_matrix(associations)
elif isinstance(associations, pd.DataFrame):
matrix = associations.values
matrix[matrix < weigthMin] = 0
max = np.amax(matrix)
matrixD = max - matrix
np.fill_diagonal(matrixD, 0)
if not openTsne:
tsne = TSNE(metric='precomputed',
perplexity=perplexity,
random_state=random).fit_transform(matrixD)
x = tsne[:, 0]
y = tsne[:, 1]
else:
n = matrixD.shape[0]
narrayD = np.zeros((n, n))
for j in range(n):
narrayD[j, :] = matrixD[j, :]
affinities_train = PerplexityBasedNN(narrayD,
perplexity=perplexity,
method='predefined',
metric="predefined",
random_state=random)
init_train = initialization.random(narrayD, random_state=random)
embedding_train = TSNEEmbedding(init_train,
affinities=affinities_train)
embedding = embedding_train.optimize(n_iter=250,
exaggeration=12,
momentum=0.5)
x = [x[0] for x in embedding]
y = [x[1] for x in embedding]
for i in range(len(matrixD) - 1):
for j in range(i + 1, len(matrixD)):
if matrixD[i, j] < max:
plt.plot([x[i], x[j]], [y[i], y[j]],
'bo-',
linewidth=0.5,
markersize=1)
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__)