kennard-stone 3.0.0rc1

A method for selecting samples by spreading the training data evenly.

Kennard Stone

What is this?

This is an algorithm for evenly partitioning data in a scikit-learn-like interface. (See References for details of the algorithm.)

How to install

PyPI

pip install kennard-stone

The project site is here.

Anaconda

conda install -c conda-forge kennard-stone

The project site is here.

You need numpy>=1.20 and scikit-learn to run.

How to use

You can use them like scikit-learn.

See examples for details.

In the following, X denotes an arbitrary explanatory variable and y an arbitrary objective variable. And, estimator indicates an arbitrary prediction model that conforms to scikit-learn.

train_test_split

kennard_stone

from kennard_stone import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

scikit-learn

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=334
)

KFold

kennard_stone

from kennard_stone import KFold

# Always shuffled and uniquely determined for a data set.
kf = KFold(n_splits=5)
for i_train, i_test in kf.split(X, y):
    X_train = X[i_train]
    y_train = y[i_train]
    X_test = X[i_test]
    y_test = y[i_test]

scikit-learn

from sklearn.model_selection import KFold

kf = KFold(n_splits=5, shuffle=True, random_state=334)
for i_train, i_test in kf.split(X, y):
    X_train = X[i_train]
    y_train = y[i_train]
    X_test = X[i_test]
    y_test = y[i_test]

Other usages

If you ever specify cv in scikit-learn, you can assign KFold objects to it and apply it to various functions.

An example is cross_validate.

kennard_stone

from kennard_stone import KFold
from sklearn.model_selection import cross_validate

kf = KFold(n_splits=5)
print(cross_validate(estimator, X, y, cv=kf))

scikit-learn

from sklearn.model_selection import KFold
from sklearn.model_selection import cross_validate

kf = KFold(n_splits=5, shuffle=True, random_state=334)
print(cross_validate(estimator, X, y, cv=kf))

OR

from sklearn.model_selection import cross_validate

print(cross_validate(estimator, X, y, cv=5))

Notes

There is no notion of random_state or shuffle because the partitioning is determined uniquely for the dataset. If these arguments are included, they do not cause an error. They simply have no effect on the result. Please be careful.

If you want to run the notebook in examples directory, you will need to additionally install 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', '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. To solve this problem, I have implemented parallelization and optimized the algorithm since v2.1.0. n_jobs can be specified for parallelization as in the scikit-learn-like api.

# parallelization KFold
kf = KFold(n_splits=5, n_jobs=-1)

# parallelization train_test_split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, n_jobs=-1
)

The parallelization is used when calculating the distance matrix, so it doesn't conflict with something like cross_validate in parallel when using KFold.

# OK: does not conflict each other
cross_validate(estimator, X, y, cv=KFold(5, n_jobs=-1), n_jobs=-1)

Using GPU

If you have a GPU and have installed pytorch, you can use it to calculate Minkowski distances (Manhattan, Euclidean, and Chebyshev distances).

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, device="cuda"
)

LICENSE

MIT Licence

Copyright (c) 2021 yu9824

References

Papers

• R. W. Kennard & L. A. Stone (1969) Computer Aided Design of Experiments, Technometrics, 11:1, 137-148, DOI: 10.1080/00401706.1969.10490666

Sites

• https://datachemeng.com/trainingtestdivision/ (Japanese site)

Histories

v2.0.0 (deprecated)

• Define Extended Kennard-Stone algorithm (multi-class) i.e. Improve KFold algorithm.
• Delete alternate argument in KFold.
• Delete requirements of pandas.

v2.0.1

• Fix bug with Python3.7.

v2.1.0 (deprecated)

• Optimize algorithm
• Deal with Large number of data.
  • parallel calculation when calculating distance (Add n_jobs argument)
  • replacing recursive functions with for-loops
• Add other than "euclidean" calculation methods (Add metric argument)

v2.1.1 (deprecated)

• Fix bug when metric="nan_euclidean".

v2.1.2 (deprecated)

• Fix details.
  • Update docstrings and typings.

v2.1.3 (deprecated)

• Fix details.
  • Update some typings. (You have access to a list of strings that can be used in the metric.)

v2.1.4

• Fix bug when metric=="seuclidean" and "mahalanobis"
  • Add some tests to check all metrics.
• Add requirements numpy>=1.20

v2.1.5

• Delete "klusinski" metric to support scipy>=1.11

v2.1.6

• Improve typing in kennard_stone.train_test_split
• Add some docstrings.

v2.2.0

• Supports GPU calculations. (when metric is 'euclidean', 'manhattan', 'chebyshev' and 'minkowski')
• Supports Python 3.12

v2.2.1

• Fix setup.cfg
• Update 'typing'