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https://github.com/trent-b/iterative-stratification
scikit-learn cross validators for iterative stratification of multilabel data
https://github.com/trent-b/iterative-stratification
cross-validation multilabel multilabel-classification scikit-learn stratification
Last synced: 22 days ago
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scikit-learn cross validators for iterative stratification of multilabel data
- Host: GitHub
- URL: https://github.com/trent-b/iterative-stratification
- Owner: trent-b
- License: bsd-3-clause
- Created: 2018-02-04T00:32:10.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-10-12T16:36:02.000Z (22 days ago)
- Last Synced: 2024-10-12T20:37:00.367Z (22 days ago)
- Topics: cross-validation, multilabel, multilabel-classification, scikit-learn, stratification
- Language: Python
- Size: 45.9 KB
- Stars: 848
- Watchers: 6
- Forks: 75
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
[](https://travis-ci.org/vfdev-5/iterative-stratification)
[](https://coveralls.io/github/vfdev-5/iterative-stratification?branch=master)
# iterative-stratification
iterative-stratification is a project that provides [scikit-learn](http://scikit-learn.org/) compatible cross validators with stratification for multilabel data.
Presently scikit-learn provides several cross validators with stratification. However, these cross validators do not offer the ability to stratify _multilabel_ data. This iterative-stratification project offers implementations of MultilabelStratifiedKFold, MultilabelRepeatedStratifiedKFold, and MultilabelStratifiedShuffleSplit with a base algorithm for stratifying multilabel data described in the following paper:
Sechidis K., Tsoumakas G., Vlahavas I. (2011) On the Stratification of Multi-Label Data. In: Gunopulos D., Hofmann T., Malerba D., Vazirgiannis M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science, vol 6913. Springer, Berlin, Heidelberg.
## Requirements
iterative-stratification has been tested under Python 3.4 through 3.9 with the following dependencies:
- scipy(>=0.13.3)
- numpy(>=1.8.2)
- scikit-learn(>=0.19.0)
## Installation
iterative-stratification is currently available on the PyPi repository and can be installed via pip:
```
pip install iterative-stratification
```
## Toy Examples
The multilabel cross validators that this package provides may be used with the scikit-learn API in the same manner as any other cross validators. For example, these cross validators may be passed to [cross_val_score](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html) or [cross_val_predict](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html). Below are some toy examples of the direct use of the multilabel cross validators.
### MultilabelStratifiedKFold
```python
from iterstrat.ml_stratifiers import MultilabelStratifiedKFold
import numpy as np
X = np.array([[1,2], [3,4], [1,2], [3,4], [1,2], [3,4], [1,2], [3,4]])
y = np.array([[0,0], [0,0], [0,1], [0,1], [1,1], [1,1], [1,0], [1,0]])
mskf = MultilabelStratifiedKFold(n_splits=2, shuffle=True, random_state=0)
for train_index, test_index in mskf.split(X, y):
print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
```
Output:
```
TRAIN: [0 3 4 6] TEST: [1 2 5 7]
TRAIN: [1 2 5 7] TEST: [0 3 4 6]
```
### RepeatedMultilabelStratifiedKFold
```python
from iterstrat.ml_stratifiers import RepeatedMultilabelStratifiedKFold
import numpy as np
X = np.array([[1,2], [3,4], [1,2], [3,4], [1,2], [3,4], [1,2], [3,4]])
y = np.array([[0,0], [0,0], [0,1], [0,1], [1,1], [1,1], [1,0], [1,0]])
rmskf = RepeatedMultilabelStratifiedKFold(n_splits=2, n_repeats=2, random_state=0)
for train_index, test_index in rmskf.split(X, y):
print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
```
Output:
```
TRAIN: [0 3 4 6] TEST: [1 2 5 7]
TRAIN: [1 2 5 7] TEST: [0 3 4 6]
TRAIN: [0 1 4 5] TEST: [2 3 6 7]
TRAIN: [2 3 6 7] TEST: [0 1 4 5]
```
### MultilabelStratifiedShuffleSplit
```python
from iterstrat.ml_stratifiers import MultilabelStratifiedShuffleSplit
import numpy as np
X = np.array([[1,2], [3,4], [1,2], [3,4], [1,2], [3,4], [1,2], [3,4]])
y = np.array([[0,0], [0,0], [0,1], [0,1], [1,1], [1,1], [1,0], [1,0]])
msss = MultilabelStratifiedShuffleSplit(n_splits=3, test_size=0.5, random_state=0)
for train_index, test_index in msss.split(X, y):
print("TRAIN:", train_index, "TEST:", test_index)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
```
Output:
```
TRAIN: [1 2 5 7] TEST: [0 3 4 6]
TRAIN: [2 3 6 7] TEST: [0 1 4 5]
TRAIN: [1 2 5 6] TEST: [0 3 4 7]
```