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Update docstrings and typings
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@yu9824
yu9824 committed Aug 23, 2023
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24 changes: 24 additions & 0 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -136,6 +136,30 @@ If these arguments are included, they do not cause an error. They simply have no

If you want to run the notebook in example directory, you will need to additionally download `pandas`, `matplotlib`, `seaborn`, `tqdm`, and `jupyter` other than the packages in requirements.txt.

## Distance metrics

See the documentation of

- `scipy.spatial.distance.pdist`
https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.pdist.html
- `sklearn.metrics.pairwise_distances`
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances.html

Valid values for metric are:

- From scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1',
'l2', 'manhattan']. These metrics support sparse matrix inputs.
['nan_euclidean'] but it does not yet support sparse matrices.
- From scipy.spatial.distance: ['braycurtis', 'canberra',
'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard',
'kulsinski', 'mahalanobis', 'minkowski', 'rogerstanimoto',
'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath',
'sqeuclidean', 'yule'] See the documentation for
scipy.spatial.distance for details on these metrics.
These metrics do not support sparse matrix inputs.

, by default "euclidean"

## Parallelization (since v2.1.0)

This algorithm is very computationally intensive and takes a lot of time.
Expand Down
168 changes: 90 additions & 78 deletions kennard_stone/kennard_stone.py
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Expand Up @@ -2,7 +2,7 @@
Copyright © 2021 yu9824
"""

from typing import overload, Union, Optional, Generator
from typing import overload, Union, Optional, Generator, Callable

# The fllowing has deprecated in Python >= 3.9
from typing import List, Set
Expand All @@ -23,17 +23,13 @@
from sklearn.utils import check_array


# TODO: sphinx documentation？
# TODO: parallelization


class KFold(_BaseKFold):
@overload
def __init__(
self,
n_splits: int = 5,
*,
metric: str = "euclidean",
metric: Union[str, Callable] = "euclidean",
n_jobs: Optional[int] = None,
) -> None:
pass
Expand All @@ -42,7 +38,7 @@ def __init__(
self,
n_splits: int = 5,
*,
metric: str = "euclidean",
metric: Union[str, Callable] = "euclidean",
n_jobs: Optional[int] = None,
random_state: None = None,
shuffle: None = None,
Expand All @@ -54,26 +50,39 @@ def __init__(
n_splits : int, optional
Number of folds. Must be at least 2., by default 5
metric : str, optional
metric : Union[str, Callable], optional
The distance metric to use. See the documentation of
`sklearn.metrics.pairwise_distances` for valid values.
- `scipy.spatial.distance.pdist`
https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.pdist.html
- `sklearn.metrics.pairwise_distances`
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances.html
for valid values.
, by default "euclidean"
=============== ========================================
metric Function
=============== ========================================
'cityblock' metrics.pairwise.manhattan_distances
'cosine' metrics.pairwise.cosine_distances
'euclidean' metrics.pairwise.euclidean_distances
'haversine' metrics.pairwise.haversine_distances
'l1' metrics.pairwise.manhattan_distances
'l2' metrics.pairwise.euclidean_distances
'manhattan' metrics.pairwise.manhattan_distances
'nan_euclidean' metrics.pairwise.nan_euclidean_distances
=============== ========================================
Valid values for metric are:
- From scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1',
'l2', 'manhattan']. These metrics support sparse matrix inputs.
['nan_euclidean'] but it does not yet support sparse matrices.
- From scipy.spatial.distance: ['braycurtis', 'canberra',
'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard',
'kulsinski', 'mahalanobis', 'minkowski', 'rogerstanimoto',
'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath',
'sqeuclidean', 'yule'] See the documentation for
scipy.spatial.distance for details on these metrics.
These metrics do not support sparse matrix inputs.
n_jobs : int, optional
The number of parallel jobs., by default None
random_state : None, deprecated
This parameter is deprecated and has no effect
because the algorithm is deterministic.
shuffle : None, deprecated
This parameter is deprecated and has no effect
because the algorithm is deterministic.
"""
super().__init__(n_splits=n_splits, shuffle=False, random_state=None)
self.metric = metric
Expand Down Expand Up @@ -146,7 +155,7 @@ def _iter_indices(self, X, y=None, groups=None):

for _ in range(self.get_n_splits()):
ind_test = indexes[:n_test]
ind_train = indexes[n_test : (n_test + n_train)]
ind_train = indexes[n_test : (n_test + n_train)] # noqa: E203
yield ind_train, ind_test


Expand All @@ -155,7 +164,7 @@ def train_test_split(
*arrays,
test_size: Optional[Union[float, int]] = None,
train_size: Optional[Union[float, int]] = None,
metric: str = "euclidean",
metric: Union[str, Callable] = "euclidean",
n_jobs: Optional[int] = None,
) -> list:
pass
Expand All @@ -165,7 +174,7 @@ def train_test_split(
*arrays,
test_size: Optional[Union[float, int]] = None,
train_size: Optional[Union[float, int]] = None,
metric: str = "euclidean",
metric: Union[str, Callable] = "euclidean",
n_jobs: Optional[int] = None,
random_state: None = None,
shuffle: None = None,
Expand All @@ -174,7 +183,7 @@ def train_test_split(
Kennard-Stone algorithm.
Data partitioning by the Kennard-Stone algorithm is performed based on the
first element to be input.
first element to be input.
Parameters
----------
Expand All @@ -195,27 +204,40 @@ def train_test_split(
absolute number of train samples. If None, the value is automatically
set to the complement of the test size., by default None
metric : str, optional
metric : Union[str, Callable], optional
The distance metric to use. See the documentation of
`sklearn.metrics.pairwise_distances` for valid values.
- `scipy.spatial.distance.pdist`
https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.pdist.html
- `sklearn.metrics.pairwise_distances`
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances.html
for valid values.
, by default "euclidean"
=============== ========================================
metric Function
=============== ========================================
'cityblock' metrics.pairwise.manhattan_distances
'cosine' metrics.pairwise.cosine_distances
'euclidean' metrics.pairwise.euclidean_distances
'haversine' metrics.pairwise.haversine_distances
'l1' metrics.pairwise.manhattan_distances
'l2' metrics.pairwise.euclidean_distances
'manhattan' metrics.pairwise.manhattan_distances
'nan_euclidean' metrics.pairwise.nan_euclidean_distances
=============== ========================================
Valid values for metric are:
- From scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1',
'l2', 'manhattan']. These metrics support sparse matrix inputs.
['nan_euclidean'] but it does not yet support sparse matrices.
- From scipy.spatial.distance: ['braycurtis', 'canberra',
'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard',
'kulsinski', 'mahalanobis', 'minkowski', 'rogerstanimoto',
'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath',
'sqeuclidean', 'yule'] See the documentation for
scipy.spatial.distance for details on these metrics.
These metrics do not support sparse matrix inputs.
n_jobs : int, optional
The number of parallel jobs., by default None
random_state : None, deprecated
This parameter is deprecated and has no effect
because the algorithm is deterministic.
shuffle : None, deprecated
This parameter is deprecated and has no effect
because the algorithm is deterministic.
Returns
-------
splitting : list, length=2 * len(arrays)
Expand Down Expand Up @@ -269,7 +291,7 @@ def __init__(
self,
n_groups: int = 1,
scale: bool = True,
metric: str = "euclidean",
metric: Union[str, Callable] = "euclidean",
n_jobs: Optional[int] = None,
) -> None:
"""The root program of the Kennard-Stone algorithm.
Expand All @@ -282,26 +304,39 @@ def __init__(
scale : bool, optional
scaling X or not, by default True
metric : str, optional
metric : Union[str, Callable], optional
The distance metric to use. See the documentation of
`sklearn.metrics.pairwise_distances` for valid values.
- `scipy.spatial.distance.pdist`
https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.pdist.html
- `sklearn.metrics.pairwise_distances`
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances.html
for valid values.
, by default "euclidean"
=============== ========================================
metric Function
=============== ========================================
'cityblock' metrics.pairwise.manhattan_distances
'cosine' metrics.pairwise.cosine_distances
'euclidean' metrics.pairwise.euclidean_distances
'haversine' metrics.pairwise.haversine_distances
'l1' metrics.pairwise.manhattan_distances
'l2' metrics.pairwise.euclidean_distances
'manhattan' metrics.pairwise.manhattan_distances
'nan_euclidean' metrics.pairwise.nan_euclidean_distances
=============== ========================================
Valid values for metric are:
- From scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1',
'l2', 'manhattan']. These metrics support sparse matrix inputs.
['nan_euclidean'] but it does not yet support sparse matrices.
- From scipy.spatial.distance: ['braycurtis', 'canberra',
'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard',
'kulsinski', 'mahalanobis', 'minkowski', 'rogerstanimoto',
'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath',
'sqeuclidean', 'yule'] See the documentation for
scipy.spatial.distance for details on these metrics.
These metrics do not support sparse matrix inputs.
n_jobs : int, optional
The number of parallel jobs., by default None
random_state : None, deprecated
This parameter is deprecated and has no effect
because the algorithm is deterministic.
shuffle : None, deprecated
This parameter is deprecated and has no effect
because the algorithm is deterministic.
"""
self.n_groups = n_groups
self.scale = scale
Expand Down Expand Up @@ -441,27 +476,4 @@ def get_indexes(self, X) -> List[List[int]]:


if __name__ == "__main__":
from sklearn.model_selection import cross_validate
from sklearn.datasets import load_diabetes, fetch_california_housing
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

data = fetch_california_housing(as_frame=True)
# data = load_diabetes(as_frame=True)
X = data.data
y = data.target

# ks = _KennardStone(n_groups=2, scale=True, n_jobs=-1)
# ks = _KennardStone(n_groups=1, scale=True, n_jobs=-1)
# ks.get_indexes(X)

X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, n_jobs=-1
)
rf = RandomForestRegressor(n_jobs=-1, random_state=334)
rf.fit(X_train, y_train)
y_pred_on_test = rf.predict(X_test)
print(mean_squared_error(y_test, y_pred_on_test, squared=False))

# kf = KFold(n_splits=5, n_jobs=-1)
# print(cross_validate(rf, X, y, scoring="neg_mean_squared_error", cv=kf))
pass

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