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LLE utilizing kNN for sample points #29752

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5e52d30
black made linting changes
Aug 30, 2024
6265d68
undid sentencification or error message at 297
Sep 6, 2024
ed87bf8
Merge branch 'main' into fix/issue-29715
davidcherney Sep 6, 2024
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30 changes: 10 additions & 20 deletions sklearn/manifold/_locally_linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,10 +112,10 @@ def barycenter_kneighbors_graph(X, n_neighbors, reg=1e-3, n_jobs=None):
sklearn.neighbors.kneighbors_graph
sklearn.neighbors.radius_neighbors_graph
"""
knn = NearestNeighbors(n_neighbors=n_neighbors + 1, n_jobs=n_jobs).fit(X)
knn = NearestNeighbors(n_neighbors=n_neighbors, n_jobs=n_jobs).fit(X)
X = knn._fit_X
n_samples = knn.n_samples_fit_
ind = knn.kneighbors(X, return_distance=False)[:, 1:]
ind = knn.kneighbors(return_distance=False)
data = barycenter_weights(X, X, ind, reg=reg)
indptr = np.arange(0, n_samples * n_neighbors + 1, n_neighbors)
return csr_matrix((data.ravel(), ind.ravel(), indptr), shape=(n_samples, n_samples))
Expand Down Expand Up @@ -212,19 +212,18 @@ def _locally_linear_embedding(
random_state=None,
n_jobs=None,
):
nbrs = NearestNeighbors(n_neighbors=n_neighbors + 1, n_jobs=n_jobs)
nbrs.fit(X)
nbrs = NearestNeighbors(n_neighbors=n_neighbors, n_jobs=n_jobs).fit(X)
X = nbrs._fit_X

N, d_in = X.shape

if n_components > d_in:
raise ValueError(
"output dimension must be less than or equal to input dimension"
"Output dimension must be less than or equal to input dimension."
)
if n_neighbors >= N:
raise ValueError(
"Expected n_neighbors <= n_samples, but n_samples = %d, n_neighbors = %d"
"Expected n_neighbors < n_samples, but n_samples = %d, n_neighbors = %d."
% (N, n_neighbors)
)

Expand All @@ -250,15 +249,12 @@ def _locally_linear_embedding(

if n_neighbors <= n_components + dp:
raise ValueError(
"for method='hessian', n_neighbors must be "
"For method='hessian', n_neighbors must be "
"greater than "
"[n_components * (n_components + 3) / 2]"
"[n_components * (n_components + 3) / 2]."
)

neighbors = nbrs.kneighbors(
X, n_neighbors=n_neighbors + 1, return_distance=False
)
neighbors = neighbors[:, 1:]
neighbors = nbrs.kneighbors(n_neighbors=n_neighbors, return_distance=False)

Yi = np.empty((n_neighbors, 1 + n_components + dp), dtype=np.float64)
Yi[:, 0] = 1
Expand Down Expand Up @@ -300,10 +296,7 @@ def _locally_linear_embedding(
if n_neighbors < n_components:
raise ValueError("modified LLE requires n_neighbors >= n_components")

neighbors = nbrs.kneighbors(
X, n_neighbors=n_neighbors + 1, return_distance=False
)
neighbors = neighbors[:, 1:]
neighbors = nbrs.kneighbors(n_neighbors=n_neighbors, return_distance=False)

# find the eigenvectors and eigenvalues of each local covariance
# matrix. We want V[i] to be a [n_neighbors x n_neighbors] matrix,
Expand Down Expand Up @@ -400,10 +393,7 @@ def _locally_linear_embedding(
M[i, i] += s_i

elif method == "ltsa":
neighbors = nbrs.kneighbors(
X, n_neighbors=n_neighbors + 1, return_distance=False
)
neighbors = neighbors[:, 1:]
neighbors = nbrs.kneighbors(n_neighbors=n_neighbors, return_distance=False)

M = M_container_constructor((N, N), dtype=np.float64)

Expand Down