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MAINT Improve Nearest Neighbor documentation + code consistency #19793

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merged 3 commits into from
Apr 5, 2021
Merged

MAINT Improve Nearest Neighbor documentation + code consistency #19793

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a47eecd
Improve Nearest Neighbor documentation + code consistency
chrisyeh96 Mar 31, 2021
4904e86
Revert n_samples_fit => n_indexed
chrisyeh96 Apr 3, 2021
4e1492b
Use consistent description of *neighbors_graph return value
chrisyeh96 Apr 3, 2021
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59 changes: 27 additions & 32 deletions sklearn/neighbors/_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -444,20 +444,19 @@ def _fit(self, X, y=None):
self.n_samples_fit_ = X.data.shape[0]
return self

if self.effective_metric_ == 'precomputed':
if self.metric == 'precomputed':
X = _check_precomputed(X)
# Precomputed matrix X must be squared
if X.shape[0] != X.shape[1]:
raise ValueError("Precomputed matrix must be square."
" Input is a {}x{} matrix."
.format(X.shape[0], X.shape[1]))
self.n_features_in_ = X.shape[1]

n_samples = X.shape[0]
if n_samples == 0:
raise ValueError("n_samples must be greater than 0")

# Precomputed matrix X must be squared
if self.metric == 'precomputed' and X.shape[0] != X.shape[1]:
raise ValueError("Precomputed matrix must be a square matrix."
" Input is a {}x{} matrix."
.format(X.shape[0], X.shape[1]))

if issparse(X):
if self.algorithm not in ('auto', 'brute'):
warnings.warn("cannot use tree with sparse input: "
Expand Down Expand Up @@ -514,14 +513,12 @@ def _fit(self, X, y=None):
if self.n_neighbors <= 0:
raise ValueError(
"Expected n_neighbors > 0. Got %d" %
self.n_neighbors
)
else:
if not isinstance(self.n_neighbors, numbers.Integral):
raise TypeError(
"n_neighbors does not take %s value, "
"enter integer value" %
type(self.n_neighbors))
self.n_neighbors)
elif not isinstance(self.n_neighbors, numbers.Integral):
raise TypeError(
"n_neighbors does not take %s value, "
"enter integer value" %
type(self.n_neighbors))

return self

Expand Down Expand Up @@ -654,18 +651,16 @@ class from an array representing our data set and ask who's
elif n_neighbors <= 0:
raise ValueError(
"Expected n_neighbors > 0. Got %d" %
n_neighbors
)
else:
if not isinstance(n_neighbors, numbers.Integral):
raise TypeError(
"n_neighbors does not take %s value, "
"enter integer value" %
type(n_neighbors))
n_neighbors)
elif not isinstance(n_neighbors, numbers.Integral):
raise TypeError(
"n_neighbors does not take %s value, "
"enter integer value" %
type(n_neighbors))

if X is not None:
query_is_train = False
if self.effective_metric_ == 'precomputed':
if self.metric == 'precomputed':
X = _check_precomputed(X)
else:
X = self._validate_data(X, accept_sparse='csr', reset=False)
Expand All @@ -687,7 +682,7 @@ class from an array representing our data set and ask who's
n_jobs = effective_n_jobs(self.n_jobs)
chunked_results = None
if (self._fit_method == 'brute' and
self.effective_metric_ == 'precomputed' and issparse(X)):
self.metric == 'precomputed' and issparse(X)):
results = _kneighbors_from_graph(
X, n_neighbors=n_neighbors,
return_distance=return_distance)
Expand Down Expand Up @@ -793,8 +788,8 @@ def kneighbors_graph(self, X=None, n_neighbors=None,
Returns
-------
A : sparse-matrix of shape (n_queries, n_samples_fit)
`n_samples_fit` is the number of samples in the fitted data
`A[i, j]` is assigned the weight of edge that connects `i` to `j`.
`n_samples_fit` is the number of samples in the fitted data.
`A[i, j]` gives the weight of the edge connecting `i` to `j`.
The matrix is of CSR format.

Examples
Expand Down Expand Up @@ -980,7 +975,7 @@ class from an array representing our data set and ask who's

if X is not None:
query_is_train = False
if self.effective_metric_ == 'precomputed':
if self.metric == 'precomputed':
X = _check_precomputed(X)
else:
X = self._validate_data(X, accept_sparse='csr', reset=False)
Expand All @@ -992,7 +987,7 @@ class from an array representing our data set and ask who's
radius = self.radius

if (self._fit_method == 'brute' and
self.effective_metric_ == 'precomputed' and issparse(X)):
self.metric == 'precomputed' and issparse(X)):
results = _radius_neighbors_from_graph(
X, radius=radius, return_distance=return_distance)

Expand Down Expand Up @@ -1116,9 +1111,9 @@ def radius_neighbors_graph(self, X=None, radius=None, mode='connectivity',
Returns
-------
A : sparse-matrix of shape (n_queries, n_samples_fit)
`n_samples_fit` is the number of samples in the fitted data
`A[i, j]` is assigned the weight of edge that connects `i` to `j`.
The matrix if of format CSR.
`n_samples_fit` is the number of samples in the fitted data.
`A[i, j]` gives the weight of the edge connecting `i` to `j`.
The matrix is of CSR format.

Examples
--------
Expand Down