961 lines
32 KiB
Python
961 lines
32 KiB
Python
"""
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The :mod:`sklearn.cross_validation` module includes utilities for cross-
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validation and performance evaluation.
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"""
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# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
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# Gael Varoquaux <gael.varoquaux@normalesup.org>,
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# Olivier Grisel <olivier.grisel@ensta.org>
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# License: BSD Style.
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from itertools import combinations
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from math import ceil, factorial
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import operator
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import numpy as np
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import scipy.sparse as sp
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from .base import is_classifier, clone
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from .utils import check_arrays, check_random_state
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from .utils.fixes import unique
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from .externals.joblib import Parallel, delayed
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class LeaveOneOut(object):
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"""Leave-One-Out cross validation iterator.
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Provides train/test indices to split data in train test sets. Each
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sample is used once as a test set (singleton) while the remaining
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samples form the training set.
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Due to the high number of test sets (which is the same as the
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number of samples) this cross validation method can be very costly.
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For large datasets one should favor KFold, StratifiedKFold or
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ShuffleSplit.
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Parameters
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==========
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n: int
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Total number of elements
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indices: boolean, optional (default True)
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Return train/test split as arrays of indices, rather than a boolean
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mask array. Integer indices are required when dealing with sparse
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matrices, since those cannot be indexed by boolean masks.
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Examples
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========
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>>> from sklearn import cross_validation
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>>> X = np.array([[1, 2], [3, 4]])
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>>> y = np.array([1, 2])
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>>> loo = cross_validation.LeaveOneOut(2)
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>>> len(loo)
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2
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>>> print loo
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sklearn.cross_validation.LeaveOneOut(n=2)
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>>> for train_index, test_index in loo:
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... print "TRAIN:", train_index, "TEST:", test_index
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... X_train, X_test = X[train_index], X[test_index]
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... y_train, y_test = y[train_index], y[test_index]
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... print X_train, X_test, y_train, y_test
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TRAIN: [1] TEST: [0]
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[[3 4]] [[1 2]] [2] [1]
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TRAIN: [0] TEST: [1]
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[[1 2]] [[3 4]] [1] [2]
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See also
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========
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LeaveOneLabelOut for splitting the data according to explicit,
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domain-specific stratification of the dataset.
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"""
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def __init__(self, n, indices=True):
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self.n = n
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self.indices = indices
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def __iter__(self):
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n = self.n
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for i in xrange(n):
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test_index = np.zeros(n, dtype=np.bool)
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test_index[i] = True
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train_index = np.logical_not(test_index)
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if self.indices:
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ind = np.arange(n)
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train_index = ind[train_index]
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test_index = ind[test_index]
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yield train_index, test_index
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def __repr__(self):
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return '%s.%s(n=%i)' % (
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self.__class__.__module__,
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self.__class__.__name__,
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self.n,
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)
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def __len__(self):
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return self.n
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class LeavePOut(object):
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"""Leave-P-Out cross validation iterator
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Provides train/test indices to split data in train test sets. The
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test set is built using p samples while the remaining samples form
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the training set.
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Due to the high number of iterations which grows with the number of
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samples this cross validation method can be very costly. For large
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|
datasets one should favor KFold, StratifiedKFold or ShuffleSplit.
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|
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|
Parameters
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===========
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n: int
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Total number of elements
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p: int
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Size of the test sets
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indices: boolean, optional (default True)
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Return train/test split as arrays of indices, rather than a boolean
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mask array. Integer indices are required when dealing with sparse
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matrices, since those cannot be indexed by boolean masks.
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Examples
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========
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>>> from sklearn import cross_validation
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>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
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>>> y = np.array([1, 2, 3, 4])
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>>> lpo = cross_validation.LeavePOut(4, 2)
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>>> len(lpo)
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6
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>>> print lpo
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sklearn.cross_validation.LeavePOut(n=4, p=2)
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>>> for train_index, test_index in lpo:
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... print "TRAIN:", train_index, "TEST:", test_index
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... X_train, X_test = X[train_index], X[test_index]
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... y_train, y_test = y[train_index], y[test_index]
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TRAIN: [2 3] TEST: [0 1]
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TRAIN: [1 3] TEST: [0 2]
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TRAIN: [1 2] TEST: [0 3]
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TRAIN: [0 3] TEST: [1 2]
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TRAIN: [0 2] TEST: [1 3]
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TRAIN: [0 1] TEST: [2 3]
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"""
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def __init__(self, n, p, indices=True):
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self.n = n
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self.p = p
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self.indices = indices
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def __iter__(self):
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n = self.n
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p = self.p
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comb = combinations(range(n), p)
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for idx in comb:
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test_index = np.zeros(n, dtype=np.bool)
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test_index[np.array(idx)] = True
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train_index = np.logical_not(test_index)
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if self.indices:
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ind = np.arange(n)
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train_index = ind[train_index]
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test_index = ind[test_index]
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yield train_index, test_index
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def __repr__(self):
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return '%s.%s(n=%i, p=%i)' % (
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self.__class__.__module__,
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self.__class__.__name__,
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self.n,
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self.p,
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)
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def __len__(self):
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return (factorial(self.n) / factorial(self.n - self.p)
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/ factorial(self.p))
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class KFold(object):
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"""K-Folds cross validation iterator
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Provides train/test indices to split data in train test sets. Split
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dataset into k consecutive folds (without shuffling).
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Each fold is then used a validation set once while the k - 1 remaining
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fold form the training set.
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Parameters
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----------
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n: int
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Total number of elements
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k: int
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Number of folds
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indices: boolean, optional (default True)
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Return train/test split as arrays of indices, rather than a boolean
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mask array. Integer indices are required when dealing with sparse
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matrices, since those cannot be indexed by boolean masks.
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Examples
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--------
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>>> from sklearn import cross_validation
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>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
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>>> y = np.array([1, 2, 3, 4])
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>>> kf = cross_validation.KFold(4, k=2)
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>>> len(kf)
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2
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>>> print kf
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sklearn.cross_validation.KFold(n=4, k=2)
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>>> for train_index, test_index in kf:
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... print "TRAIN:", train_index, "TEST:", test_index
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... X_train, X_test = X[train_index], X[test_index]
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... y_train, y_test = y[train_index], y[test_index]
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TRAIN: [2 3] TEST: [0 1]
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TRAIN: [0 1] TEST: [2 3]
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Notes
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-----
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All the folds have size trunc(n_samples / n_folds), the last one has the
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complementary.
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See also
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--------
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StratifiedKFold: take label information into account to avoid building
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folds with imbalanced class distributions (for binary or multiclass
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classification tasks).
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"""
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def __init__(self, n, k, indices=True):
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assert k > 0, ValueError('Cannot have number of folds k below 1.')
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assert k <= n, ValueError('Cannot have number of folds k=%d, '
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'greater than the number '
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'of samples: %d.' % (k, n))
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self.n = n
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self.k = k
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self.indices = indices
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def __iter__(self):
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n = self.n
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k = self.k
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j = ceil(n / k)
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for i in xrange(k):
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test_index = np.zeros(n, dtype=np.bool)
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if i < k - 1:
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test_index[i * j:(i + 1) * j] = True
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else:
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test_index[i * j:] = True
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train_index = np.logical_not(test_index)
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if self.indices:
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ind = np.arange(n)
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train_index = ind[train_index]
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test_index = ind[test_index]
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yield train_index, test_index
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def __repr__(self):
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return '%s.%s(n=%i, k=%i)' % (
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self.__class__.__module__,
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self.__class__.__name__,
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self.n,
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self.k,
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)
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def __len__(self):
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return self.k
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class StratifiedKFold(object):
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"""Stratified K-Folds cross validation iterator
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Provides train/test indices to split data in train test sets.
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This cross-validation object is a variation of KFold, which
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returns stratified folds. The folds are made by preserving
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the percentage of samples for each class.
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Parameters
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----------
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y: array, [n_samples]
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Samples to split in K folds
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k: int
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Number of folds
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indices: boolean, optional (default True)
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Return train/test split as arrays of indices, rather than a boolean
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|
mask array. Integer indices are required when dealing with sparse
|
|
matrices, since those cannot be indexed by boolean masks.
|
|
|
|
Examples
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--------
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|
>>> from sklearn import cross_validation
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>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
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>>> y = np.array([0, 0, 1, 1])
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>>> skf = cross_validation.StratifiedKFold(y, k=2)
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>>> len(skf)
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2
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>>> print skf
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sklearn.cross_validation.StratifiedKFold(labels=[0 0 1 1], k=2)
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>>> for train_index, test_index in skf:
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... print "TRAIN:", train_index, "TEST:", test_index
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... X_train, X_test = X[train_index], X[test_index]
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... y_train, y_test = y[train_index], y[test_index]
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TRAIN: [1 3] TEST: [0 2]
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TRAIN: [0 2] TEST: [1 3]
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Notes
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-----
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All the folds have size trunc(n_samples / n_folds), the last one has the
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complementary.
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"""
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def __init__(self, y, k, indices=True):
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y = np.asarray(y)
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n = y.shape[0]
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assert k > 0, ValueError('Cannot have number of folds k below 1.')
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assert k <= n, ValueError('Cannot have number of folds k=%d, '
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'greater than the number '
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'of samples: %d.' % (k, n))
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_, y_sorted = unique(y, return_inverse=True)
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min_labels = np.min(np.bincount(y_sorted))
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assert k <= min_labels, ValueError(
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'Cannot have number of folds k=%d, smaller than %d, the minimum '
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'number of labels for any class.' % (k, min_labels))
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self.y = y
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self.k = k
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self.indices = indices
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def __iter__(self):
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y = self.y.copy()
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k = self.k
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n = y.size
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idx = np.argsort(y)
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for i in xrange(k):
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test_index = np.zeros(n, dtype=np.bool)
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test_index[idx[i::k]] = True
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train_index = np.logical_not(test_index)
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if self.indices:
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ind = np.arange(n)
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train_index = ind[train_index]
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test_index = ind[test_index]
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yield train_index, test_index
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def __repr__(self):
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return '%s.%s(labels=%s, k=%i)' % (
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self.__class__.__module__,
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self.__class__.__name__,
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self.y,
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self.k,
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)
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def __len__(self):
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return self.k
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|
|
|
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class LeaveOneLabelOut(object):
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"""Leave-One-Label_Out cross-validation iterator
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Provides train/test indices to split data according to a third-party
|
|
provided label. This label information can be used to encode arbitrary
|
|
domain specific stratifications of the samples as integers.
|
|
|
|
For instance the labels could be the year of collection of the samples
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|
and thus allow for cross-validation against time-based splits.
|
|
|
|
Parameters
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----------
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labels : array-like of int with shape (n_samples,)
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|
Arbitrary domain-specific stratification of the data to be used
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|
to draw the splits.
|
|
|
|
indices: boolean, optional (default True)
|
|
Return train/test split as arrays of indices, rather than a boolean
|
|
mask array. Integer indices are required when dealing with sparse
|
|
matrices, since those cannot be indexed by boolean masks.
|
|
|
|
Examples
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|
----------
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|
>>> from sklearn import cross_validation
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>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
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>>> y = np.array([1, 2, 1, 2])
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>>> labels = np.array([1, 1, 2, 2])
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>>> lol = cross_validation.LeaveOneLabelOut(labels)
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>>> len(lol)
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2
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>>> print lol
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sklearn.cross_validation.LeaveOneLabelOut(labels=[1 1 2 2])
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>>> for train_index, test_index in lol:
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... print "TRAIN:", train_index, "TEST:", test_index
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... X_train, X_test = X[train_index], X[test_index]
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... y_train, y_test = y[train_index], y[test_index]
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... print X_train, X_test, y_train, y_test
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TRAIN: [2 3] TEST: [0 1]
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[[5 6]
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[7 8]] [[1 2]
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[3 4]] [1 2] [1 2]
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TRAIN: [0 1] TEST: [2 3]
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[[1 2]
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[3 4]] [[5 6]
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[7 8]] [1 2] [1 2]
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"""
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def __init__(self, labels, indices=True):
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self.labels = labels
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self.n_unique_labels = unique(labels).size
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self.indices = indices
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|
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def __iter__(self):
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# We make a copy here to avoid side-effects during iteration
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labels = np.array(self.labels, copy=True)
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for i in unique(labels):
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test_index = np.zeros(len(labels), dtype=np.bool)
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test_index[labels == i] = True
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train_index = np.logical_not(test_index)
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if self.indices:
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ind = np.arange(len(labels))
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train_index = ind[train_index]
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test_index = ind[test_index]
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yield train_index, test_index
|
|
|
|
def __repr__(self):
|
|
return '%s.%s(labels=%s)' % (
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self.__class__.__module__,
|
|
self.__class__.__name__,
|
|
self.labels,
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|
)
|
|
|
|
def __len__(self):
|
|
return self.n_unique_labels
|
|
|
|
|
|
class LeavePLabelOut(object):
|
|
"""Leave-P-Label_Out cross-validation iterator
|
|
|
|
Provides train/test indices to split data according to a third-party
|
|
provided label. This label information can be used to encode arbitrary
|
|
domain specific stratifications of the samples as integers.
|
|
|
|
For instance the labels could be the year of collection of the samples
|
|
and thus allow for cross-validation against time-based splits.
|
|
|
|
The difference between LeavePLabelOut and LeaveOneLabelOut is that
|
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the former builds the test sets with all the samples assigned to
|
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``p`` different values of the labels while the latter uses samples
|
|
all assigned the same labels.
|
|
|
|
Parameters
|
|
----------
|
|
labels : array-like of int with shape (n_samples,)
|
|
Arbitrary domain-specific stratification of the data to be used
|
|
to draw the splits.
|
|
|
|
p : int
|
|
Number of samples to leave out in the test split.
|
|
|
|
indices: boolean, optional (default True)
|
|
Return train/test split as arrays of indices, rather than a boolean
|
|
mask array. Integer indices are required when dealing with sparse
|
|
matrices, since those cannot be indexed by boolean masks.
|
|
|
|
Examples
|
|
----------
|
|
>>> from sklearn import cross_validation
|
|
>>> X = np.array([[1, 2], [3, 4], [5, 6]])
|
|
>>> y = np.array([1, 2, 1])
|
|
>>> labels = np.array([1, 2, 3])
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|
>>> lpl = cross_validation.LeavePLabelOut(labels, p=2)
|
|
>>> len(lpl)
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|
3
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|
>>> print lpl
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|
sklearn.cross_validation.LeavePLabelOut(labels=[1 2 3], p=2)
|
|
>>> for train_index, test_index in lpl:
|
|
... print "TRAIN:", train_index, "TEST:", test_index
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|
... X_train, X_test = X[train_index], X[test_index]
|
|
... y_train, y_test = y[train_index], y[test_index]
|
|
... print X_train, X_test, y_train, y_test
|
|
TRAIN: [2] TEST: [0 1]
|
|
[[5 6]] [[1 2]
|
|
[3 4]] [1] [1 2]
|
|
TRAIN: [1] TEST: [0 2]
|
|
[[3 4]] [[1 2]
|
|
[5 6]] [2] [1 1]
|
|
TRAIN: [0] TEST: [1 2]
|
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[[1 2]] [[3 4]
|
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[5 6]] [1] [2 1]
|
|
"""
|
|
|
|
def __init__(self, labels, p, indices=True):
|
|
self.labels = labels
|
|
self.unique_labels = unique(self.labels)
|
|
self.n_unique_labels = self.unique_labels.size
|
|
self.p = p
|
|
self.indices = indices
|
|
|
|
def __iter__(self):
|
|
# We make a copy here to avoid side-effects during iteration
|
|
labels = np.array(self.labels, copy=True)
|
|
unique_labels = unique(labels)
|
|
comb = combinations(range(self.n_unique_labels), self.p)
|
|
|
|
for idx in comb:
|
|
test_index = np.zeros(labels.size, dtype=np.bool)
|
|
idx = np.array(idx)
|
|
for l in unique_labels[idx]:
|
|
test_index[labels == l] = True
|
|
train_index = np.logical_not(test_index)
|
|
if self.indices:
|
|
ind = np.arange(labels.size)
|
|
train_index = ind[train_index]
|
|
test_index = ind[test_index]
|
|
yield train_index, test_index
|
|
|
|
def __repr__(self):
|
|
return '%s.%s(labels=%s, p=%s)' % (
|
|
self.__class__.__module__,
|
|
self.__class__.__name__,
|
|
self.labels,
|
|
self.p,
|
|
)
|
|
|
|
def __len__(self):
|
|
return (factorial(self.n_unique_labels) /
|
|
factorial(self.n_unique_labels - self.p) /
|
|
factorial(self.p))
|
|
|
|
|
|
class Bootstrap(object):
|
|
"""Random sampling with replacement cross-validation iterator
|
|
|
|
Provides train/test indices to split data in train test sets
|
|
while resampling the input n_bootstraps times: each time a new
|
|
random split of the data is performed and then samples are drawn
|
|
(with replacement) on each side of the split to build the training
|
|
and test sets.
|
|
|
|
Note: contrary to other cross-validation strategies, bootstrapping
|
|
will allow some samples to occur several times in each splits. However
|
|
a sample that occurs in the train split will never occur in the test
|
|
split and vice-versa.
|
|
|
|
If you want each sample to occur at most once you should probably
|
|
use ShuffleSplit cross validation instead.
|
|
|
|
Parameters
|
|
----------
|
|
n : int
|
|
Total number of elements in the dataset.
|
|
|
|
n_bootstraps : int (default is 3)
|
|
Number of bootstrapping iterations
|
|
|
|
n_train : int or float (default is 0.5)
|
|
If int, number of samples to include in the training split
|
|
(should be smaller than the total number of samples passed
|
|
in the dataset).
|
|
|
|
If float, should be between 0.0 and 1.0 and represent the
|
|
proportion of the dataset to include in the train split.
|
|
|
|
n_test : int or float or None (default is None)
|
|
If int, number of samples to include in the training set
|
|
(should be smaller than the total number of samples passed
|
|
in the dataset).
|
|
|
|
If float, should be between 0.0 and 1.0 and represent the
|
|
proportion of the dataset to include in the test split.
|
|
|
|
If None, n_test is set as the complement of n_train.
|
|
|
|
random_state : int or RandomState
|
|
Pseudo number generator state used for random sampling.
|
|
|
|
Examples
|
|
--------
|
|
>>> from sklearn import cross_validation
|
|
>>> bs = cross_validation.Bootstrap(9, random_state=0)
|
|
>>> len(bs)
|
|
3
|
|
>>> print bs
|
|
Bootstrap(9, n_bootstraps=3, n_train=5, n_test=4, random_state=0)
|
|
>>> for train_index, test_index in bs:
|
|
... print "TRAIN:", train_index, "TEST:", test_index
|
|
...
|
|
TRAIN: [1 8 7 7 8] TEST: [0 3 0 5]
|
|
TRAIN: [5 4 2 4 2] TEST: [6 7 1 0]
|
|
TRAIN: [4 7 0 1 1] TEST: [5 3 6 5]
|
|
|
|
See also
|
|
--------
|
|
ShuffleSplit: cross validation using random permutations.
|
|
"""
|
|
|
|
# Static marker to be able to introspect the CV type
|
|
indices = True
|
|
|
|
def __init__(self, n, n_bootstraps=3, n_train=0.5, n_test=None,
|
|
random_state=None):
|
|
self.n = n
|
|
self.n_bootstraps = n_bootstraps
|
|
|
|
if isinstance(n_train, float) and n_train >= 0.0 and n_train <= 1.0:
|
|
self.n_train = ceil(n_train * n)
|
|
elif isinstance(n_train, int):
|
|
self.n_train = n_train
|
|
else:
|
|
raise ValueError("Invalid value for n_train: %r" % n_train)
|
|
if self.n_train > n:
|
|
raise ValueError("n_train=%d should not be larger than n=%d" %
|
|
(self.n_train, n))
|
|
|
|
if isinstance(n_test, float) and n_test >= 0.0 and n_test <= 1.0:
|
|
self.n_test = ceil(n_test * n)
|
|
elif isinstance(n_test, int):
|
|
self.n_test = n_test
|
|
elif n_test is None:
|
|
self.n_test = self.n - self.n_train
|
|
else:
|
|
raise ValueError("Invalid value for n_test: %r" % n_test)
|
|
if self.n_test > n:
|
|
raise ValueError("n_test=%d should not be larger than n=%d" %
|
|
(self.n_test, n))
|
|
|
|
self.random_state = random_state
|
|
|
|
def __iter__(self):
|
|
rng = self.random_state = check_random_state(self.random_state)
|
|
for i in range(self.n_bootstraps):
|
|
# random partition
|
|
permutation = rng.permutation(self.n)
|
|
ind_train = permutation[:self.n_train]
|
|
ind_test = permutation[self.n_train:self.n_train + self.n_test]
|
|
|
|
# bootstrap in each split individually
|
|
train = rng.randint(0, self.n_train, size=(self.n_train,))
|
|
test = rng.randint(0, self.n_test, size=(self.n_test,))
|
|
yield ind_train[train], ind_test[test]
|
|
|
|
def __repr__(self):
|
|
return ('%s(%d, n_bootstraps=%d, n_train=%d, n_test=%d, '
|
|
'random_state=%d)' % (
|
|
self.__class__.__name__,
|
|
self.n,
|
|
self.n_bootstraps,
|
|
self.n_train,
|
|
self.n_test,
|
|
self.random_state,
|
|
))
|
|
|
|
def __len__(self):
|
|
return self.n_bootstraps
|
|
|
|
|
|
class ShuffleSplit(object):
|
|
"""Random permutation cross-validation iterator.
|
|
|
|
Yields indices to split data into training and test sets.
|
|
|
|
Note: contrary to other cross-validation strategies, random splits
|
|
do not guarantee that all folds will be different, although this is
|
|
still very likely for sizeable datasets.
|
|
|
|
Parameters
|
|
----------
|
|
n : int
|
|
Total number of elements in the dataset.
|
|
|
|
n_iterations : int (default 10)
|
|
Number of re-shuffling & splitting iterations.
|
|
|
|
test_fraction : float (default 0.1)
|
|
Should be between 0.0 and 1.0 and represent the proportion of
|
|
the dataset to include in the test split.
|
|
|
|
indices : boolean, optional (default True)
|
|
Return train/test split as arrays of indices, rather than a boolean
|
|
mask array. Integer indices are required when dealing with sparse
|
|
matrices, since those cannot be indexed by boolean masks.
|
|
|
|
random_state : int or RandomState
|
|
Pseudo-random number generator state used for random sampling.
|
|
|
|
Examples
|
|
----------
|
|
>>> from sklearn import cross_validation
|
|
>>> rs = cross_validation.ShuffleSplit(4, n_iterations=3,
|
|
... test_fraction=.25, random_state=0)
|
|
>>> len(rs)
|
|
3
|
|
>>> print rs
|
|
... # doctest: +ELLIPSIS
|
|
ShuffleSplit(4, n_iterations=3, test_fraction=0.25, indices=True, ...)
|
|
>>> for train_index, test_index in rs:
|
|
... print "TRAIN:", train_index, "TEST:", test_index
|
|
...
|
|
TRAIN: [2 3 1] TEST: [0]
|
|
TRAIN: [0 2 1] TEST: [3]
|
|
TRAIN: [3 0 2] TEST: [1]
|
|
|
|
See also
|
|
--------
|
|
Bootstrap: cross-validation using re-sampling with replacement.
|
|
"""
|
|
|
|
def __init__(self, n, n_iterations=10, test_fraction=0.1,
|
|
indices=True, random_state=None):
|
|
self.n = n
|
|
self.n_iterations = n_iterations
|
|
self.test_fraction = test_fraction
|
|
self.random_state = random_state
|
|
self.indices = indices
|
|
|
|
def __iter__(self):
|
|
rng = self.random_state = check_random_state(self.random_state)
|
|
n_test = ceil(self.test_fraction * self.n)
|
|
for i in range(self.n_iterations):
|
|
# random partition
|
|
permutation = rng.permutation(self.n)
|
|
ind_train = permutation[:-n_test]
|
|
ind_test = permutation[-n_test:]
|
|
|
|
if self.indices:
|
|
yield ind_train, ind_test
|
|
else:
|
|
train_mask = np.zeros(self.n, dtype=np.bool)
|
|
train_mask[ind_train] = True
|
|
test_mask = np.zeros(self.n, dtype=np.bool)
|
|
test_mask[ind_test] = True
|
|
yield train_mask, test_mask
|
|
|
|
def __repr__(self):
|
|
return ('%s(%d, n_iterations=%d, test_fraction=%s, indices=%s, '
|
|
'random_state=%s)' % (
|
|
self.__class__.__name__,
|
|
self.n,
|
|
self.n_iterations,
|
|
str(self.test_fraction),
|
|
self.indices,
|
|
self.random_state,
|
|
))
|
|
|
|
def __len__(self):
|
|
return self.n_iterations
|
|
|
|
|
|
##############################################################################
|
|
|
|
def _cross_val_score(estimator, X, y, score_func, train, test):
|
|
"""Inner loop for cross validation"""
|
|
if y is None:
|
|
estimator.fit(X[train])
|
|
if score_func is None:
|
|
return estimator.score(X[test])
|
|
else:
|
|
return score_func(X[test])
|
|
else:
|
|
estimator.fit(X[train], y[train])
|
|
if score_func is None:
|
|
return estimator.score(X[test], y[test])
|
|
else:
|
|
return score_func(y[test], estimator.predict(X[test]))
|
|
|
|
|
|
def cross_val_score(estimator, X, y=None, score_func=None, cv=None, n_jobs=1,
|
|
verbose=0):
|
|
"""Evaluate a score by cross-validation
|
|
|
|
Parameters
|
|
----------
|
|
estimator: estimator object implementing 'fit'
|
|
The object to use to fit the data
|
|
|
|
X: array-like of shape at least 2D
|
|
The data to fit.
|
|
|
|
y: array-like, optional
|
|
The target variable to try to predict in the case of
|
|
supervised learning.
|
|
|
|
score_func: callable, optional
|
|
callable taking as arguments the fitted estimator, the
|
|
test data (X_test) and the test target (y_test) if y is
|
|
not None.
|
|
|
|
cv: cross-validation generator, optional
|
|
A cross-validation generator. If None, a 3-fold cross
|
|
validation is used or 3-fold stratified cross-validation
|
|
when y is supplied and estimator is a classifier.
|
|
|
|
n_jobs: integer, optional
|
|
The number of CPUs to use to do the computation. -1 means
|
|
'all CPUs'.
|
|
|
|
verbose: integer, optional
|
|
The verbosity level
|
|
"""
|
|
X, y = check_arrays(X, y, sparse_format='csr')
|
|
cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
|
|
if score_func is None:
|
|
assert hasattr(estimator, 'score'), ValueError(
|
|
"If no score_func is specified, the estimator passed "
|
|
"should have a 'score' method. The estimator %s "
|
|
"does not." % estimator)
|
|
# We clone the estimator to make sure that all the folds are
|
|
# independent, and that it is pickle-able.
|
|
scores = Parallel(n_jobs=n_jobs, verbose=verbose)(
|
|
delayed(_cross_val_score)(clone(estimator), X, y, score_func,
|
|
train, test)
|
|
for train, test in cv)
|
|
return np.array(scores)
|
|
|
|
|
|
def _permutation_test_score(estimator, X, y, cv, score_func):
|
|
"""Auxilary function for permutation_test_score"""
|
|
avg_score = []
|
|
for train, test in cv:
|
|
avg_score.append(score_func(y[test],
|
|
estimator.fit(X[train],
|
|
y[train]).predict(X[test])))
|
|
return np.mean(avg_score)
|
|
|
|
|
|
def _shuffle(y, labels, random_state):
|
|
"""Return a shuffled copy of y eventually shuffle among same labels."""
|
|
if labels is None:
|
|
ind = random_state.permutation(y.size)
|
|
else:
|
|
ind = np.arange(labels.size)
|
|
for label in np.unique(labels):
|
|
this_mask = (labels == label)
|
|
ind[this_mask] = random_state.permutation(ind[this_mask])
|
|
return y[ind]
|
|
|
|
|
|
def check_cv(cv, X=None, y=None, classifier=False):
|
|
"""Input checker utility for building a CV in a user friendly way.
|
|
|
|
Parameters
|
|
===========
|
|
cv: an integer, a cv generator instance, or None
|
|
The input specifying which cv generator to use. It can be an
|
|
integer, in which case it is the number of folds in a KFold,
|
|
None, in which case 3 fold is used, or another object, that
|
|
will then be used as a cv generator.
|
|
|
|
X: 2D ndarray
|
|
the data the cross-val object will be applied on
|
|
|
|
y: 1D ndarray
|
|
the target variable for a supervised learning problem
|
|
|
|
classifier: boolean optional
|
|
whether the task is a classification task, in which case
|
|
stratified KFold will be used.
|
|
"""
|
|
is_sparse = sp.issparse(X)
|
|
if cv is None:
|
|
cv = 3
|
|
if operator.isNumberType(cv):
|
|
if classifier:
|
|
cv = StratifiedKFold(y, cv, indices=is_sparse)
|
|
else:
|
|
if not is_sparse:
|
|
n_samples = len(X)
|
|
else:
|
|
n_samples = X.shape[0]
|
|
cv = KFold(n_samples, cv, indices=is_sparse)
|
|
if is_sparse and not getattr(cv, "indices", True):
|
|
raise ValueError("Sparse data require indices-based cross validation"
|
|
" generator, got: %r", cv)
|
|
return cv
|
|
|
|
|
|
def permutation_test_score(estimator, X, y, score_func, cv=None,
|
|
n_permutations=100, n_jobs=1, labels=None,
|
|
random_state=0, verbose=0):
|
|
"""Evaluate the significance of a cross-validated score with permutations
|
|
|
|
Parameters
|
|
----------
|
|
estimator: estimator object implementing 'fit'
|
|
The object to use to fit the data
|
|
|
|
X: array-like of shape at least 2D
|
|
The data to fit.
|
|
|
|
y: array-like
|
|
The target variable to try to predict in the case of
|
|
supervised learning.
|
|
|
|
score_func: callable
|
|
Callable taking as arguments the test targets (y_test) and
|
|
the predicted targets (y_pred) and returns a float. The score
|
|
functions are expected to return a bigger value for a better result
|
|
otherwise the returned value does not correspond to a p-value (see
|
|
Returns below for further details).
|
|
|
|
cv : integer or crossvalidation generator, optional
|
|
If an integer is passed, it is the number of fold (default 3).
|
|
Specific crossvalidation objects can be passed, see
|
|
sklearn.cross_validation module for the list of possible objects
|
|
|
|
n_jobs: integer, optional
|
|
The number of CPUs to use to do the computation. -1 means
|
|
'all CPUs'.
|
|
|
|
labels: array-like of shape [n_samples] (optional)
|
|
Labels constrain the permutation among groups of samples with
|
|
a same label.
|
|
|
|
random_state: RandomState or an int seed (0 by default)
|
|
A random number generator instance to define the state of the
|
|
random permutations generator.
|
|
|
|
verbose: integer, optional
|
|
The verbosity level
|
|
|
|
Returns
|
|
-------
|
|
score: float
|
|
The true score without permuting targets.
|
|
|
|
permutation_scores : array, shape = [n_permutations]
|
|
The scores obtained for each permutations.
|
|
|
|
pvalue: float
|
|
The returned value equals p-value if `score_func` returns bigger
|
|
numbers for better scores (e.g., zero_one). If `score_func` is rather a
|
|
loss function (i.e. when lower is better such as with
|
|
`mean_square_error`) then this is actually the complement of the
|
|
p-value: 1 - p-value.
|
|
|
|
References
|
|
----------
|
|
This function implements Test 1 in:
|
|
|
|
Ojala and Garriga. Permutation Tests for Studying Classifier
|
|
Performance. The Journal of Machine Learning Research (2010)
|
|
vol. 11
|
|
|
|
"""
|
|
X, y = check_arrays(X, y, sparse_format='csr')
|
|
cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
|
|
|
|
random_state = check_random_state(random_state)
|
|
|
|
# We clone the estimator to make sure that all the folds are
|
|
# independent, and that it is pickle-able.
|
|
score = _permutation_test_score(clone(estimator), X, y, cv, score_func)
|
|
permutation_scores = Parallel(n_jobs=n_jobs, verbose=verbose)(
|
|
delayed(_permutation_test_score)(clone(estimator), X,
|
|
_shuffle(y, labels, random_state),
|
|
cv, score_func)
|
|
for _ in range(n_permutations))
|
|
permutation_scores = np.array(permutation_scores)
|
|
pvalue = (np.sum(permutation_scores >= score) + 1.0) / (n_permutations + 1)
|
|
return score, permutation_scores, pvalue
|
|
|
|
|
|
permutation_test_score.__test__ = False # to avoid a pb with nosetests
|