#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# =============================================================================
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# | . . |_ _ ___ ___ ___ | | ___ ___ | | |_/ / _____ __
# | |\/| | | | / __|/ _ \/ _ \ | |/ _ \ / _ \| | ___ \/ _ \ \/ /
# | | | | |_| \__ \ __/ (_) | | | (_) | (_) | | |_/ / (_) > <
# \_| |_/\__,_|___/\___|\___/ \_/\___/ \___/|_\____/ \___/_/\_\
#
# @author: Nicolas Karasiak
# @site: www.karasiak.net
# @git: www.github.com/nkarasiak/MuseoToolBox
# =============================================================================
"""
The :mod:`museotoolbox.cross_validation` module gathers cross-validation classes.
"""
from . import _sample_selection
[docs]class LeaveOneOut(_sample_selection._cv_manager):
"""
Generate a Cross-Validation using a Stratified Leave One Out.
Note : :class:`~LeaveOneOut` is equivalent to :class:`~museotoolbox.cross_validation.RandomStratifiedKFold` with ``valid_size=1`` and ``n_splits=False``.
Parameters
----------
n_repeats : int or bool, optional (default=False).
If False : will iterate as many times as the smallest class.
If int : will iterate the number of times given in n_splits.
random_state : integer or None, optional (default=False).
If int, random_state is the seed used by the random number generator;
If None, the random number generator is created with ``time.time()``.
verbose : integer or False, optional (default=False).
Controls the verbosity: the higher the value is, the more the messages are detailed.
"""
[docs] def __init__(self,
n_repeats=False,
random_state=False,
verbose=False):
super().__init__(_sample_selection.randomPerClass, valid_size=1,
n_repeats=n_repeats, random_state=random_state, verbose=verbose)
[docs]class LeavePSubGroupOut(_sample_selection._cv_manager):
"""
Generate a Cross-Validation using subgroup (each group belong to a unique label).
Parameters
----------
valid_size : float, default 0.5.
From 0 to 1.
n_repeats : int or bool, optional (default=False).
If False, n_splits is 1/valid_size (default : 1/0.5 = 2).
If int : will iterate the number of times given in n_splits.
random_state : integer or None, optional (default=False).
If int, random_state is the seed used by the random number generator;
If None, the random number generator is created with ``time.time()``.
verbose : integer or False, optional (default=False).
Controls the verbosity: the higher the value is, the more the messages are detailed.
"""
[docs] def __init__(self,
valid_size=0.5,
n_repeats=False,
random_state=False,
verbose=False):
if isinstance(valid_size, float):
if valid_size > 1 or valid_size < 0:
raise ValueError('Percent must be between 0 and 1')
else:
raise ValueError(
'Percent must be between 0 and 1 and must be a float')
if n_repeats == False:
n_repeats = int(1 / valid_size)
super().__init__(_sample_selection.groupCV, valid_size=valid_size,
n_repeats=n_repeats, random_state=random_state, verbose=verbose)
[docs]class LeaveOneSubGroupOut(_sample_selection._cv_manager):
"""
Generate a Cross-Validation by subgroup.
Parameters
----------
n_repeats : int or bool, optional (default=False).
If False : will iterate as many times as the smallest number of groups.
If int : will iterate the number of times given in n_splits.
random_state : integer or None, optional (default=False).
If int, random_state is the seed used by the random number generator;
If None, the random number generator is created with ``time.time()``.
verbose : integer or False, optional (default=False).
Controls the verbosity: the higher the value is, the more the messages are detailed.
"""
[docs] def __init__(self,
n_repeats=False,
random_state=False,
verbose=False):
super().__init__(_sample_selection.groupCV, valid_size=1, n_repeats=n_repeats,
random_state=random_state, verbose=verbose)
[docs]class SpatialLeaveAsideOut(_sample_selection._cv_manager):
"""
Generate a Cross-Validation using the farthest distance between the training and validation samples.
Parameters
----------
distance_matrix : numpy.ndarray, shape [n_samples, n_samples].
Array got from function samplingMethods.getdistance_matrixForDistanceCV(inRaster,inVector)
valid_size : float, default 0.5.
The percentage of validaton to keep : from 0 to 1.
n_repeats : int or bool, optional (default=False).
If False, n_repeats is 1/valid_size (default : 1/0.5 = 2)
If int : will iterate the number of times given in n_repeats.
random_state : integer or None, optional (default=None).
If int, random_state is the seed used by the random number generator;
If None, the random number generator is created with ``time.time()``.
verbose : integer or False, optional (default=False).
Controls the verbosity: the higher the value is, the more the messages are detailed.
References
----------
See "Combining ensemble modeling and remote sensing for mapping
individual tree species at high spatial resolution" : https://doi.org/10.1016/j.foreco.2013.07.059.
"""
[docs] def __init__(self,
distance_matrix,
valid_size=0.5,
n_repeats=False,
random_state=False,
verbose=False):
super().__init__(_sample_selection.distanceCV, distance_matrix=distance_matrix,
valid_size=valid_size, n_repeats=n_repeats, random_state=random_state, verbose=verbose)
[docs]class SpatialLeaveOneSubGroupOut(_sample_selection._cv_manager):
"""
Generate a Cross-Validation with Spatial Leave-One-Out method.
Parameters
----------
distance_matrix : numpy.ndarray, shape [n_samples, n_samples].
Array got from function :func:`museotoolbox.vector_tools.get_distance_matrix`
distance_thresold : int.
In pixels.
distance_label : None or array.
If array, got from function :func:`museotoolbox.vector_tools.get_distance_matrix`
random_state : integer or None, optional (default=None).
If int, random_state is the seed used by the random number generator;
If None, the random number generator is created with ``time.time()``.
verbose : integer or False, optional (default=False).
Controls the verbosity: the higher the value is, the more the messages are detailed.
See also
--------
museotoolbox.processing.get_distance_matrix : to get distance matrix and label.
"""
[docs] def __init__(self,
distance_thresold,
distance_matrix,
distance_label,
random_state=False,
verbose=False):
super().__init__(_sample_selection.distanceCV, distance_matrix=distance_matrix,
distance_thresold=distance_thresold,
distance_label=distance_label,
random_state=random_state, verbose=verbose)
[docs]class SpatialLeaveOneOut(_sample_selection._cv_manager):
"""
Generate a Cross-Validation with a stratified spatial Leave-One-Out method.
Parameters
----------
distance_matrix : numpy.ndarray, shape [n_samples, n_samples].
Array got from function museotoolbox.vector_tools.get_distance_matrix(inRaster,inVector)
distance_thresold : int.
In pixels.
n_repeats: int or False, optional (default=False).
If False : will iterate as many times as the smallest number of groups.
If int : will iterate the number of times specified.
random_state : int or False, optional (default=False).
If int, random_state is the seed used by the random number generator;
If None, the random number generator is created with ``time.time()``.
verbose : integer or False, optional (default=False).
Controls the verbosity: the higher the value is, the more the messages are detailed.
See also
---------
museotoolbox.vector_tools.get_distance_matrix : to get distance matrix and label.
References
----------
See "Spatial leave‐one‐out cross‐validation for variable selection in the
presence of spatial autocorrelation" : https://doi.org/10.1111/geb.12161.
"""
[docs] def __init__(self,
distance_thresold=None,
distance_matrix=None,
n_repeats=False,
n_splits=False,
random_state=False,
verbose=False,
**kwargs):
super().__init__(_sample_selection.distanceCV, distance_matrix=distance_matrix,
distance_thresold=distance_thresold,
distance_label=False,
valid_size=1,
n_repeats=n_repeats,
n_splits=n_splits,
random_state=random_state,
verbose=verbose,
**kwargs)
[docs]class RandomStratifiedKFold(_sample_selection._cv_manager):
"""
Generate a Cross-Validation with full random selection and Stratified K-Fold (same percentange per class).
Parameters
----------
n_splits : int, optional (default=2).
Number of splits. 2 means 50% for each class at training and validation.
n_repeats : integer or False, optional (default=False)
If False, will repeat n_splits once.
valid_size : int or False, optional (default=False).
If False, valid size is ``1 / n_splits``.
random_state : integer or None, optional (default=False).
If int, random_state is the seed used by the random number generator;
If None, the random number generator is created with ``time.time()``.
verbose : integer or False, optional (default=False).
Controls the verbosity: the higher the value is, the more the messages are detailed.
Example
-------
>>> from museotoolbox.cross_validation import RandomStratifiedKFold
>>> from museotoolbox import datasets
>>> X,y = datasets.load_historical_data(return_X_y=True)
>>> RSK = RandomStratifiedKFold(n_splits=2,random_state=12,verbose=False)
>>> for tr,vl in RSK.split(X=X,y=y):
print(tr,vl)
[ 1600 1601 1605 ..., 9509 9561 10322] [ 3632 1988 11480 ..., 10321 9457 9508]
[ 1599 1602 1603 ..., 9508 9560 10321] [ 3948 10928 3490 ..., 10322 9458 9561]
"""
[docs] def __init__(self,
n_splits=2,
n_repeats=False,
valid_size=False,
random_state=False,
verbose=False):
if valid_size is False:
valid_size = 1 / n_splits
if n_repeats == False or n_repeats == 0:
n_repeats = n_splits
else:
n_repeats = n_splits * n_repeats
super().__init__(_sample_selection.randomPerClass, valid_size=valid_size,
random_state=random_state,
n_repeats=n_repeats, verbose=verbose)