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AvasamAvasam
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Remove and resolve TODO comments copied from source python files
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‎stubs/networkx/algorithms/approximation/dominating_set.pyi‎

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from ...classes.graph import Graph
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from ...utils import not_implemented_for
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from ..matching import maximal_matching
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__all__ = ["min_weighted_dominating_set", "min_edge_dominating_set"]
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# TODO Why doesn't this algorithm work for directed graphs?
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def min_weighted_dominating_set(G: Graph, weight: str | None = None) -> set: ...
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def min_edge_dominating_set(G: Graph) -> set: ...

‎stubs/networkx/algorithms/clique.pyi‎

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from collections import defaultdict, deque
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from itertools import chain, combinations, islice
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from numpy.typing import ArrayLike
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from ..classes.graph import Graph
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from ..utils import not_implemented_for
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__all__ = [
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"find_cliques",
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def enumerate_all_cliques(G: Graph): ...
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def find_cliques(G: Graph, nodes=None): ...
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# TODO Should this also be not implemented for directed graphs?
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def find_cliques_recursive(G: Graph, nodes=None): ...
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def make_max_clique_graph(G: Graph, create_using=None): ...
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def make_clique_bipartite(G: Graph, fpos: bool | None = None, create_using=None, name=None): ...

‎stubs/networkx/algorithms/cuts.pyi‎

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from itertools import chain
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from typing import Any
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from ..classes.graph import Graph
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"volume",
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]
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# TODO STILL NEED TO UPDATE ALL THE DOCUMENTATION!
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def cut_size(G: Graph, S, T=None, weight: Any = None): ...
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def volume(G: Graph, S, weight: Any = None): ...
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def normalized_cut_size(G: Graph, S, T=None, weight: Any = None): ...
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def conductance(G: Graph, S, T=None, weight: Any = None): ...
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def edge_expansion(G: Graph, S, T=None, weight: Any = None): ...
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def mixing_expansion(G: Graph, S, T=None, weight: Any = None): ...
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# TODO What is the generalization to two arguments, S and T? Does the
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# denominator become `min(len(S), len(T))`?
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def node_expansion(G: Graph, S): ...
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# TODO What is the generalization to two arguments, S and T? Does the
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# denominator become `min(len(S), len(T))`?
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def boundary_expansion(G: Graph, S): ...

‎stubs/networkx/algorithms/distance_regular.pyi‎

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@@ -3,8 +3,6 @@ from collections.abc import Iterable
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from numpy.typing import ArrayLike
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from ..classes.graph import Graph
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from ..utils import not_implemented_for
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from .distance_measures import diameter
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__all__ = [
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"is_distance_regular",
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def is_distance_regular(G: Graph) -> bool: ...
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def global_parameters(b: ArrayLike, c: ArrayLike) -> Iterable: ...
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def intersection_array(G: Graph): ...
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# TODO There is a definition for directed strongly regular graphs.
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def is_strongly_regular(G: Graph) -> bool: ...

‎stubs/networkx/algorithms/tree/branchings.pyi‎

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# TODO: Implement method from Gabow, Galil, Spence and Tarjan:
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#
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# @article{
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# year={1986},
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# issn={0209-9683},
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# journal={Combinatorica},
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# volume={6},
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# number={2},
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# doi={10.1007/BF02579168},
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# title={Efficient algorithms for finding minimum spanning trees in
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# undirected and directed graphs},
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# url={https://doi.org/10.1007/BF02579168},
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# publisher={Springer-Verlag},
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# keywords={68 B 15; 68 C 05},
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# author={Gabow, Harold N. and Galil, Zvi and Spencer, Thomas and Tarjan,
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# Robert E.},
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# pages={109-122},
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# language={English}
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# }
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import string
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from dataclasses import dataclass, field
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from enum import Enum
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from operator import itemgetter
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from queue import PriorityQueue
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from typing import Literal
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from ...classes.digraph import DiGraph

‎stubs/networkx/conftest.pyi‎

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import sys
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import warnings
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import networkx
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import pytest
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def pytest_addoption(parser): ...
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def pytest_configure(config): ...
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def pytest_collection_modifyitems(config, items): ...
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# TODO: The warnings below need to be dealt with, but for now we silence them.
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@pytest.fixture(autouse=True)
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def set_warnings(): ...
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@pytest.fixture(autouse=True)

‎stubs/sklearn/cluster/_agglomerative.pyi‎

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@@ -49,8 +49,7 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator):
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self,
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n_clusters: None | int = 2,
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*,
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affinity: str | Callable = "deprecated", # TODO(1.4): Remove
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metric: None | str | Callable = None, # TODO(1.4): Set to "euclidean"
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metric: str | Callable = "euclidean",
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memory: None | Memory | str = None,
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connectivity: None | ArrayLike | Callable = None,
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compute_full_tree: Literal["auto"] | bool = "auto",
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self,
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n_clusters: None | int = 2,
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*,
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affinity: str | Callable = "deprecated", # TODO(1.4): Remove
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metric: None | str | Callable = None, # TODO(1.4): Set to "euclidean"
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metric: str | Callable = "euclidean",
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memory: None | Memory | str = None,
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connectivity: None | ArrayLike | Callable = None,
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compute_full_tree: Literal["auto"] | bool = "auto",

‎stubs/sklearn/covariance/_robust_covariance.pyi‎

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@@ -7,17 +7,6 @@ from numpy.random import RandomState
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from .._typing import Float, Int, MatrixLike
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from . import EmpiricalCovariance
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# Author: Virgile Fritsch <virgile.fritsch@inria.fr>
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#
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# License: BSD 3 clause
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# Minimum Covariance Determinant
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# Implementing of an algorithm by Rousseeuw & Van Driessen described in
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# (A Fast Algorithm for the Minimum Covariance Determinant Estimator,
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# 1999, American Statistical Association and the American Society
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# for Quality, TECHNOMETRICS)
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# XXX Is this really a public function? It's not listed in the docs or
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# exported by sklearn.covariance. Deprecate?
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def c_step(
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X: MatrixLike,
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n_support: Int,

‎stubs/sklearn/datasets/_samples_generator.pyi‎

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tail_strength: Float = 0.5,
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random_state: RandomState | None | Int = None,
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) -> ndarray: ...
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# TODO(1.3): Change argument `data_transposed` default from True to False.
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# TODO(1.3): Deprecate data_transposed, always return data not transposed.
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def make_sparse_coded_signal(
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n_samples: Int,
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*,

‎stubs/sklearn/ensemble/_gb.pyi‎

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feature_importances_: ndarray = ...
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n_estimators_: int = ...
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# TODO(1.3): remove "deviance"
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_parameter_constraints: ClassVar[dict] = ...
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def __init__(

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