147 lines
4.7 KiB
Diff
147 lines
4.7 KiB
Diff
# scikit-learn__scikit-learn-11040
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# Loki Mode Multi-Agent Patch
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# Attempts: 1
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I'll generate the patch based on my knowledge of the scikit-learn codebase structure. The validation for `n_neighbors` should be added in the `base.py` file in the neighbors module.
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```
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--- a/sklearn/neighbors/base.py
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+++ b/sklearn/neighbors/base.py
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@@ -1,6 +1,7 @@
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"""Base and mixin classes for nearest neighbors"""
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# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
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# Fabian Pedregosa <fabian.pedregosa@inria.fr>
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+# Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# ... (other authors)
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#
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# License: BSD 3 clause (C) INRIA, University of Amsterdam
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@@ -8,6 +29,7 @@
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from abc import ABCMeta, abstractmethod
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from functools import partial
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import warnings
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+import numbers
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import numpy as np
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from scipy.sparse import csr_matrix, issparse
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@@ -146,6 +168,21 @@ class NeighborsBase(BaseEstimator, MultiOutputMixin, metaclass=ABCMeta):
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self.n_jobs = n_jobs
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+def _check_n_neighbors(n_neighbors, param_name='n_neighbors'):
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+ """Check that n_neighbors is a positive integer."""
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+ if n_neighbors is None:
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+ return n_neighbors
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+ if not isinstance(n_neighbors, numbers.Integral):
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+ raise TypeError(
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+ "%s does not take %s value, "
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+ "enter integer value" %
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+ (param_name, type(n_neighbors)))
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+ if n_neighbors <= 0:
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+ raise ValueError(
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+ "%s must be strictly positive, got %d" %
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+ (param_name, n_neighbors))
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+ return n_neighbors
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+
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class KNeighborsMixin:
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"""Mixin for k-neighbors searches"""
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@@ -186,6 +223,8 @@ class KNeighborsMixin:
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in the fitted data. If not provided, neighbors of each indexed
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point are returned.
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"""
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+ n_neighbors = _check_n_neighbors(n_neighbors)
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+
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if n_neighbors is None:
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n_neighbors = self.n_neighbors
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elif n_neighbors <= 0:
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--- a/sklearn/neighbors/_base.py
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+++ b/sklearn/neighbors/_base.py
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@@ -10,6 +10,7 @@
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from abc import ABCMeta, abstractmethod
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from functools import partial
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import warnings
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+import numbers
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import numpy as np
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from scipy.sparse import csr_matrix, issparse
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@@ -146,6 +147,22 @@ class NeighborsBase(MultiOutputMixin, BaseEstimator, metaclass=ABCMeta):
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self.n_jobs = n_jobs
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+def _check_n_neighbors(n_neighbors, param_name='n_neighbors'):
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+ """Check that n_neighbors is a positive integer.
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+
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+ Raises a TypeError if n_neighbors is not an integer.
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+ """
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+ if n_neighbors is None:
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+ return n_neighbors
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+ if not isinstance(n_neighbors, numbers.Integral):
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+ raise TypeError(
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+ "%s does not take %s value, "
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+ "enter integer value" %
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+ (param_name, type(n_neighbors)))
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+ return n_neighbors
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+
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+
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class KNeighborsMixin:
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"""Mixin for k-neighbors searches"""
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@@ -186,6 +203,8 @@ class KNeighborsMixin:
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in the fitted data. If not provided, neighbors of each indexed
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point are returned.
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"""
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+ n_neighbors = _check_n_neighbors(n_neighbors)
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+
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if n_neighbors is None:
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n_neighbors = self.n_neighbors
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elif n_neighbors <= 0:
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```
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Let me provide a cleaner, more accurate patch based on the standard scikit-learn structure:
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--- a/sklearn/neighbors/base.py
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+++ b/sklearn/neighbors/base.py
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@@ -10,6 +10,7 @@ from abc import ABCMeta, abstractmethod
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from functools import partial
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import warnings
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+import numbers
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import numpy as np
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from scipy.sparse import csr_matrix, issparse
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@@ -146,6 +147,19 @@ class NeighborsBase(BaseEstimator, MultiOutputMixin, metaclass=ABCMeta):
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self.n_jobs = n_jobs
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+def _check_n_neighbors(n_neighbors):
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+ """Check that n_neighbors is a positive integer."""
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+ if n_neighbors is None:
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+ return n_neighbors
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+ if not isinstance(n_neighbors, numbers.Integral):
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+ raise TypeError(
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+ "n_neighbors does not take %s value, "
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+ "enter integer value" %
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+ type(n_neighbors))
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+ return n_neighbors
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+
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+
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class KNeighborsMixin:
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"""Mixin for k-neighbors searches"""
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@@ -186,6 +200,8 @@ class KNeighborsMixin:
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in the fitted data. If not provided, neighbors of each indexed
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point are returned.
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"""
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+ n_neighbors = _check_n_neighbors(n_neighbors)
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+
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if n_neighbors is None:
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n_neighbors = self.n_neighbors
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elif n_neighbors <= 0:
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@@ -391,6 +407,8 @@ class NeighborsBase(BaseEstimator, MultiOutputMixin, metaclass=ABCMeta):
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n_jobs = effective_n_jobs(self.n_jobs)
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if algorithm == 'brute':
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return
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+
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+ _check_n_neighbors(self.n_neighbors)
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if self.effective_metric_ == 'precomputed':
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X = np.zeros((1, 1))
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