191 lines
5.8 KiB
Python
191 lines
5.8 KiB
Python
# Sebastian Raschka 2014-2020
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# mlxtend Machine Learning Library Extensions
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#
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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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from sklearn.model_selection import train_test_split
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class RandomHoldoutSplit(object):
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"""Train/Validation set splitter for sklearn's GridSearchCV etc.
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Provides train/validation set indices to split a dataset
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into train/validation sets using random indices.
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Parameters
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----------
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valid_size : float (default: 0.5)
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Proportion of examples that being assigned as
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validation examples. 1-`valid_size` will then automatically
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be assigned as training set examples.
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random_seed : int (default: None)
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The random seed for splitting the data
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into training and validation set partitions.
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stratify : bool (default: False)
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True or False, whether to perform a stratified
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split or not
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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/RandomHoldoutSplit/
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"""
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def __init__(self, valid_size=0.5, random_seed=None, stratify=False):
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self.valid_size = valid_size
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self.random_seed = random_seed
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self.stratify = stratify
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def split(self, X, y, groups=None):
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"""Generate indices to split data into training and test set.
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Parameters
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----------
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X : array-like, shape (num_examples, num_features)
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Training data, where num_examples is the number of
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training examples and num_features is the number of features.
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y : array-like, shape (num_examples,)
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The target variable for supervised learning problems.
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Stratification is done based on the y labels.
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groups : object
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Always ignored, exists for compatibility.
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Yields
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------
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train_index : ndarray
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The training set indices for that split.
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valid_index : ndarray
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The validation set indices for that split.
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"""
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ind = np.arange(X.shape[0])
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if self.stratify:
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train_index, valid_index, _, _ = \
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train_test_split(ind, y,
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test_size=self.valid_size,
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shuffle=True,
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stratify=y,
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random_state=self.random_seed)
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else:
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train_index, valid_index, _, _ = \
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train_test_split(ind, y,
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test_size=self.valid_size,
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shuffle=True,
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stratify=y,
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random_state=self.random_seed)
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for i in range(1):
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yield train_index, valid_index
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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.
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y : object
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Always ignored, exists for compatibility.
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groups : object
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Always ignored, exists for compatibility.
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Returns
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-------
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n_splits : 1
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Returns the number of splitting iterations in the cross-validator.
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Always returns 1.
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"""
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return 1
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class PredefinedHoldoutSplit(object):
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"""Train/Validation set splitter for sklearn's GridSearchCV etc.
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Uses user-specified train/validation set indices to split a dataset
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into train/validation sets using user-defined or random
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indices.
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Parameters
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----------
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valid_indices : array-like, shape (num_examples,)
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Indices of the training examples in the training set
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to be used for validation. All other indices in the
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training set are used to for a training subset
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for model fitting.
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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/PredefinedHoldoutSplit/
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"""
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def __init__(self, valid_indices):
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self.valid_indices = valid_indices
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def split(self, X, y, groups=None):
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"""Generate indices to split data into training and test set.
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Parameters
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----------
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X : array-like, shape (num_examples, num_features)
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Training data, where num_examples is the number of examples
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and num_features is the number of features.
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y : array-like, shape (num_examples,)
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The target variable for supervised learning problems.
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Stratification is done based on the y labels.
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groups : object
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Always ignored, exists for compatibility.
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Yields
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------
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train_index : ndarray
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The training set indices for that split.
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valid_index : ndarray
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The validation set indices for that split.
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"""
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ind = np.arange(X.shape[0])
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train_mask = np.ones(X.shape[0], dtype=np.bool)
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train_mask[self.valid_indices] = False
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valid_mask = np.where(train_mask, False, True)
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for i in range(1):
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yield ind[train_mask], ind[valid_mask]
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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.
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y : object
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Always ignored, exists for compatibility.
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groups : object
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Always ignored, exists for compatibility.
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Returns
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-------
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n_splits : 1
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Returns the number of splitting iterations in the cross-validator.
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Always returns 1.
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"""
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return 1
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