94 lines
2.5 KiB
Python
94 lines
2.5 KiB
Python
# Sebastian Raschka 2014-2020
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# mlxtend Machine Learning Library Extensions
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#
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# Bootstrap functions
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# Author: Sebastian Raschka <sebastianraschka.com>
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#
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# License: BSD 3 clause
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import numpy as np
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class BootstrapOutOfBag(object):
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"""
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Parameters
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----------
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n_splits : int (default=200)
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Number of bootstrap iterations.
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Must be larger than 1.
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random_seed : int (default=None)
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If int, random_seed is the seed used by
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the random number generator.
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Returns
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-------
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train_idx : ndarray
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The training set indices for that split.
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test_idx : ndarray
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The testing set indices for that split.
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Examples
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-----------
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For usage examples, please see
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http://rasbt.github.io/mlxtend/user_guide/evaluate/BootstrapOutOfBag/
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"""
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def __init__(self, n_splits=200, random_seed=None):
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self.random_seed = random_seed
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if not isinstance(n_splits, int) or n_splits < 1:
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raise ValueError('Number of splits must be greater than 1.')
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self.n_splits = n_splits
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def split(self, X, y=None, groups=None):
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"""
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y : array-like or None (default: None)
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Argument is not used and only included as parameter
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for compatibility, similar to `KFold` in scikit-learn.
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groups : array-like or None (default: None)
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Argument is not used and only included as parameter
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for compatibility, similar to `KFold` in scikit-learn.
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"""
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rng = np.random.RandomState(self.random_seed)
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sample_idx = np.arange(X.shape[0])
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set_idx = set(sample_idx)
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for _ in range(self.n_splits):
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train_idx = rng.choice(sample_idx,
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size=sample_idx.shape[0],
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replace=True)
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test_idx = np.array(list(set_idx - set(train_idx)))
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yield train_idx, test_idx
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def get_n_splits(self, X=None, y=None, groups=None):
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"""Returns the number of splitting iterations in the cross-validator
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Parameters
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----------
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X : object
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Always ignored, exists for compatibility with scikit-learn.
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y : object
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Always ignored, exists for compatibility with scikit-learn.
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groups : object
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Always ignored, exists for compatibility with scikit-learn.
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Returns
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-------
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n_splits : int
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Returns the number of splitting iterations in the cross-validator.
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"""
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return self.n_splits
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