def update(self, X, y=None, **fit_params):
if "relations" not in fit_params:
raise ValueError(
"Aligned UMAP requires relations between data to be "
"specified")
new_dict_relations = fit_params["relations"]
X = check_array(X)
self.__dict__ = set_aligned_params(fit_params, self.__dict__,
self.n_models_)
self.n_models_ += 1
new_mapper = UMAP(
n_neighbors=get_nth_item_or_val(self.n_neighbors, self.n_models_),
min_dist=get_nth_item_or_val(self.min_dist, self.n_models_),
n_epochs=get_nth_item_or_val(self.n_epochs, self.n_models_),
repulsion_strength=get_nth_item_or_val(self.repulsion_strength,
self.n_models_),
learning_rate=get_nth_item_or_val(self.learning_rate,
self.n_models_),
spread=get_nth_item_or_val(self.spread, self.n_models_),
negative_sample_rate=get_nth_item_or_val(self.negative_sample_rate,
self.n_models_),
local_connectivity=get_nth_item_or_val(self.local_connectivity,
self.n_models_),
set_op_mix_ratio=get_nth_item_or_val(self.set_op_mix_ratio,
self.n_models_),
unique=get_nth_item_or_val(self.unique, self.n_models_),
).fit(X)
self.mappers_ += [new_mapper]
# TODO: We can likely make this more efficient and not recompute each time
self.dict_relations_ += [invert_dict(new_dict_relations)]
if self.n_epochs is None:
n_epochs = 200
else:
n_epochs = self.n_epochs
indptr_list = numba.typed.List.empty_list(numba.types.int32[::1])
indices_list = numba.typed.List.empty_list(numba.types.int32[::1])
heads = numba.typed.List.empty_list(numba.types.int32[::1])
tails = numba.typed.List.empty_list(numba.types.int32[::1])
epochs_per_samples = numba.typed.List.empty_list(
numba.types.float64[::1])
for i, mapper in enumerate(self.mappers_):
indptr_list.append(mapper.graph_.indptr)
indices_list.append(mapper.graph_.indices)
heads.append(mapper.graph_.tocoo().row)
tails.append(mapper.graph_.tocoo().col)
if i == len(self.mappers_) - 1:
epochs_per_samples.append(
make_epochs_per_sample(mapper.graph_.tocoo().data,
n_epochs))
else:
epochs_per_samples.append(
np.full(mapper.embedding_.shape[0],
n_epochs + 1,
dtype=np.float64))
new_relations = expand_relations(self.dict_relations_)
new_regularisation_weights = build_neighborhood_similarities(
indptr_list,
indices_list,
new_relations,
)
new_embedding = init_from_existing(self.embeddings_[-1],
new_mapper.graph_,
new_dict_relations)
random_state = check_random_state(self.random_state)
rng_state = random_state.randint(INT32_MIN, INT32_MAX,
3).astype(np.int64)
self.embeddings_.append(new_embedding)
self.embeddings_ = optimize_layout_aligned_euclidean(
self.embeddings_,
self.embeddings_,
heads,
tails,
n_epochs,
epochs_per_samples,
new_regularisation_weights,
new_relations,
rng_state,
lambda_=self.alignment_regularisation,
)
def fit(self, X, y=None, **fit_params):
if "relations" not in fit_params:
raise ValueError(
"Aligned UMAP requires relations between data to be "
"specified")
self.dict_relations_ = fit_params["relations"]
assert type(self.dict_relations_) in (list, tuple)
assert type(X) in (list, tuple, np.ndarray)
assert (len(X) - 1) == (len(self.dict_relations_))
# We need n_components to be constant or this won't work
if type(self.n_components) in (list, tuple, np.ndarray):
raise ValueError(
"n_components must be a single integer, and cannot vary")
self.n_models_ = len(X)
self.mappers_ = [
UMAP(
n_neighbors=get_nth_item_or_val(self.n_neighbors, n),
min_dist=get_nth_item_or_val(self.min_dist, n),
n_epochs=get_nth_item_or_val(self.n_epochs, n),
repulsion_strength=get_nth_item_or_val(self.repulsion_strength,
n),
learning_rate=get_nth_item_or_val(self.learning_rate, n),
spread=get_nth_item_or_val(self.spread, n),
negative_sample_rate=get_nth_item_or_val(
self.negative_sample_rate, n),
local_connectivity=get_nth_item_or_val(self.local_connectivity,
n),
set_op_mix_ratio=get_nth_item_or_val(self.set_op_mix_ratio, n),
unique=get_nth_item_or_val(self.unique, n),
n_components=self.n_components,
).fit(X[n]) for n in range(self.n_models_)
]
if self.n_epochs is None:
n_epochs = 200
else:
n_epochs = self.n_epochs
window_size = fit_params.get("window_size", self.alignment_window_size)
relations = expand_relations(self.dict_relations_, window_size)
indptr_list = numba.typed.List.empty_list(numba.types.int32[::1])
indices_list = numba.typed.List.empty_list(numba.types.int32[::1])
heads = numba.typed.List.empty_list(numba.types.int32[::1])
tails = numba.typed.List.empty_list(numba.types.int32[::1])
epochs_per_samples = numba.typed.List.empty_list(
numba.types.float64[::1])
for mapper in self.mappers_:
indptr_list.append(mapper.graph_.indptr)
indices_list.append(mapper.graph_.indices)
heads.append(mapper.graph_.tocoo().row)
tails.append(mapper.graph_.tocoo().col)
epochs_per_samples.append(
make_epochs_per_sample(mapper.graph_.tocoo().data, n_epochs))
regularisation_weights = build_neighborhood_similarities(
indptr_list,
indices_list,
relations,
)
first_init = spectral_layout(
self.mappers_[0]._raw_data,
self.mappers_[0].graph_,
self.n_components,
np.random,
)
expansion = 10.0 / np.abs(first_init).max()
first_embedding = (first_init * expansion).astype(
np.float32,
order="C",
)
embeddings = numba.typed.List.empty_list(numba.types.float32[:, ::1])
embeddings.append(first_embedding)
for i in range(1, self.n_models_):
next_init = spectral_layout(
self.mappers_[i]._raw_data,
self.mappers_[i].graph_,
self.n_components,
np.random,
)
expansion = 10.0 / np.abs(next_init).max()
next_embedding = (next_init * expansion).astype(
np.float32,
order="C",
)
anchor_data = relations[i][window_size - 1]
left_anchors = anchor_data[anchor_data >= 0]
right_anchors = np.where(anchor_data >= 0)[0]
embeddings.append(
procrustes_align(
embeddings[-1],
next_embedding,
np.vstack([left_anchors, right_anchors]),
))
random_state = check_random_state(self.random_state)
rng_state = random_state.randint(INT32_MIN, INT32_MAX,
3).astype(np.int64)
self.embeddings_ = optimize_layout_aligned_euclidean(
embeddings,
embeddings,
heads,
tails,
n_epochs,
epochs_per_samples,
regularisation_weights,
relations,
rng_state,
lambda_=self.alignment_regularisation,
)
return self
