A method for selecting samples by spreading the training data evenly.
Project description
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
and scikit-learn
to run.
How to use
You can use them like scikit-learn.
See example 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 example 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.htmlsklearn.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.
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
)
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
- Define Extended Kennard-Stone algorithm (multi-class) i.e. Improve KFold algorithm.
- Delete
alternate
argument inKFold
. - Delete requirements of
pandas
.
v2.0.1
- Fix bug with Python3.7.
v2.1.0
- Optimize algorithm
- Deal with Large number of data.
- parallel calculation when calculating distance (Add
n_jobs
argument) - replacing recursive functions with for-loops
- parallel calculation when calculating distance (Add
- Add other than "euclidean" calculation methods (Add
metric
argument)
v2.1.1
- Fix bug when
metric="nan_euclidean"
.
v2.1.2
- Fix details.
- Update docstrings and typings.
Project details
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