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License: MIT License
Implementation of the INtERAcT algorithm
License: MIT License
class NearestNeighborsTree(object):
"""Nearest neighbor tree support."""
tree = None
word_series = None
cluster_series = None
def __init__(self, embedding, algorithm='kd_tree', metric='minkowski',
k=500, n_init=10, n_jobs=-1):
"""Build from embedding pd.DataFrame (index: words)."""
self.word_series = pd.Series(dict(enumerate(embedding.index.values)))
self.cluster_series = pd.Series(dict(enumerate(
cluster_vectors(
embedding.values, k=k, n_init=n_init, n_jobs=n_jobs
).labels_
)))
self.tree = build_tree(
embedding.values, algorithm=algorithm,
metric=metric, n_jobs=n_jobs
)
def kneighbors(self, X=None, k=5, mode=NeighborsMode.BOTH):
"""Get k neighbors from query points."""
if not isinstance(mode, NeighborsMode):
raise RuntimeError(
'mode as to be a value from enum NeighborsMode'
)
neighbors_dist,neighbors_indices = self.tree.kneighbors(
X=X, n_neighbors=k, return_distance=True
)
neighbors_simil = 1 / (1 + neighbors_dist)
if mode == NeighborsMode.WORDS:
return(
np.array([
_map_indices_with_series(indices, self.word_series)
for indices in neighbors_indices
]),
neighbors_dist,
neighbors_simil
)
elif mode == NeighborsMode.CLUSTERS:
return(
np.array([
_map_indices_with_series(indices, self.cluster_series)
for indices in neighbors_indices
]),
neighbors_dist,
neighbors_simil
)
elif mode == NeighborsMode.BOTH:
return (
np.array([
_map_indices_with_series(indices, self.word_series)
for indices in neighbors_indices
]),
np.array([
_map_indices_with_series(indices, self.cluster_series)
for indices in neighbors_indices
]),
neighbors_dist,
neighbors_simil
)
else:
raise RuntimeError('invalid return mode')
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