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scikit-learn cross validators for iterative stratification of multilabel data
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README.md

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iterative-stratification

iterative-stratification is a project that provides scikit-learn 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 3.7 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


The package is also installable from the Anaconda Cloud platform:

conda install -c trent-b 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 or cross_val_predict. Below are some toy examples of the direct use of the multilabel cross validators.

MultilabelStratifiedKFold

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, 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

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

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]
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