"""Class to perform under-sampling based on the instance hardness
threshold."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Dayvid Oliveira
# Christos Aridas
# License: MIT
from __future__ import division
import warnings
from collections import Counter
import numpy as np
import sklearn
from sklearn.base import ClassifierMixin
from sklearn.ensemble import RandomForestClassifier
from sklearn.externals.six import string_types
from ..base import BaseCleaningSampler
def _get_cv_splits(X, y, cv, random_state):
if hasattr(sklearn, 'model_selection'):
from sklearn.model_selection import StratifiedKFold
cv_iterator = StratifiedKFold(
n_splits=cv, shuffle=False, random_state=random_state).split(X, y)
else:
from sklearn.cross_validation import StratifiedKFold
cv_iterator = StratifiedKFold(
y, n_folds=cv, shuffle=False, random_state=random_state)
return cv_iterator
[docs]class InstanceHardnessThreshold(BaseCleaningSampler):
"""Class to perform under-sampling based on the instance hardness
threshold.
Parameters
----------
estimator : object, optional (default=RandomForestClassifier())
Classifier to be used to estimate instance hardness of the samples. By
default a :class:`sklearn.ensemble.RandomForestClassifer` will be used.
If ``str``, the choices using a string are the following: ``'knn'``,
``'decision-tree'``, ``'random-forest'``, ``'adaboost'``,
``'gradient-boosting'`` and ``'linear-svm'``. If object, an estimator
inherited from :class:`sklearn.base.ClassifierMixin` and having an
attribute :func:`predict_proba`.
.. deprecated:: 0.2
``estimator`` as a string object is deprecated from 0.2 and will be
replaced in 0.4. Use :class:`sklearn.base.ClassifierMixin` object
instead.
ratio : str, dict, or callable, optional (default='auto')
Ratio to use for resampling the data set.
- If ``str``, has to be one of: (i) ``'minority'``: resample the
minority class; (ii) ``'majority'``: resample the majority class,
(iii) ``'not minority'``: resample all classes apart of the minority
class, (iv) ``'all'``: resample all classes, and (v) ``'auto'``:
correspond to ``'all'`` with for over-sampling methods and ``'not
minority'`` for under-sampling methods. The classes targeted will be
over-sampled or under-sampled to achieve an equal number of sample
with the majority or minority class.
- If ``dict``, the keys correspond to the targeted classes. The values
correspond to the desired number of samples.
- If callable, function taking ``y`` and returns a ``dict``. The keys
correspond to the targeted classes. The values correspond to the
desired number of samples.
return_indices : bool, optional (default=False)
Whether or not to return the indices of the samples randomly
selected from the majority class.
random_state : int, RandomState instance or None, optional (default=None)
If int, ``random_state`` is the seed used by the random number
generator; If ``RandomState`` instance, random_state is the random
number generator; If ``None``, the random number generator is the
``RandomState`` instance used by ``np.random``.
cv : int, optional (default=5)
Number of folds to be used when estimating samples' instance hardness.
n_jobs : int, optional (default=1)
The number of threads to open if possible.
**kwargs:
Option for the different classifier.
.. deprecated:: 0.2
``**kwargs`` has been deprecated from 0.2 and will be replaced in
0.4. Use :class:`sklearn.base.ClassifierMixin` object instead to
pass parameter associated to an estimator.
Notes
-----
The method is based on [1]_.
Supports mutli-class resampling.
Examples
--------
>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imblearn.under_sampling import InstanceHardnessThreshold
>>> X, y = make_classification(n_classes=2, class_sep=2,
... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)
>>> print('Original dataset shape {}'.format(Counter(y)))
Original dataset shape Counter({1: 900, 0: 100})
>>> iht = InstanceHardnessThreshold(random_state=42)
>>> X_res, y_res = iht.fit_sample(X, y)
>>> print('Resampled dataset shape {}'.format(Counter(y_res)))
Resampled dataset shape Counter({1: 840, 0: 100})
References
----------
.. [1] D. Smith, Michael R., Tony Martinez, and Christophe Giraud-Carrier.
"An instance level analysis of data complexity." Machine learning
95.2 (2014): 225-256.
"""
[docs] def __init__(self,
estimator=None,
ratio='auto',
return_indices=False,
random_state=None,
cv=5,
n_jobs=1,
**kwargs):
super(InstanceHardnessThreshold, self).__init__(
ratio=ratio, random_state=random_state)
self.estimator = estimator
self.return_indices = return_indices
self.cv = cv
self.n_jobs = n_jobs
self.kwargs = kwargs
def _validate_estimator(self):
"""Private function to create the classifier"""
if (self.estimator is not None and
isinstance(self.estimator, ClassifierMixin) and
hasattr(self.estimator, 'predict_proba')):
self.estimator_ = self.estimator
elif self.estimator is None:
self.estimator_ = RandomForestClassifier(
random_state=self.random_state, n_jobs=self.n_jobs)
# To be removed in 0.4
elif (self.estimator is not None and
isinstance(self.estimator, string_types)):
# Select the appropriate classifier
warnings.warn('`estimator` will be replaced in version'
' 0.4. Use a classifier object instead of a string.',
DeprecationWarning)
if self.estimator == 'knn':
from sklearn.neighbors import KNeighborsClassifier
self.estimator_ = KNeighborsClassifier(**self.kwargs)
elif self.estimator == 'decision-tree':
from sklearn.tree import DecisionTreeClassifier
self.estimator_ = DecisionTreeClassifier(
random_state=self.random_state, **self.kwargs)
elif self.estimator == 'random-forest':
self.estimator_ = RandomForestClassifier(
random_state=self.random_state, **self.kwargs)
elif self.estimator == 'adaboost':
from sklearn.ensemble import AdaBoostClassifier
self.estimator_ = AdaBoostClassifier(
random_state=self.random_state, **self.kwargs)
elif self.estimator == 'gradient-boosting':
from sklearn.ensemble import GradientBoostingClassifier
self.estimator_ = GradientBoostingClassifier(
random_state=self.random_state, **self.kwargs)
elif self.estimator == 'linear-svm':
from sklearn.svm import SVC
self.estimator_ = SVC(probability=True,
random_state=self.random_state,
kernel='linear',
**self.kwargs)
else:
raise NotImplementedError
else:
raise ValueError('Invalid parameter `estimator`. Got {}.'.format(
type(self.estimator)))
def _sample(self, X, y):
"""Resample the dataset.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
y : ndarray, shape (n_samples, )
Corresponding label for each sample in X.
Returns
-------
X_resampled : ndarray, shape (n_samples_new, n_features)
The array containing the resampled data.
y_resampled : ndarray, shape (n_samples_new)
The corresponding label of `X_resampled`
idx_under : ndarray, shape (n_samples, )
If `return_indices` is `True`, a boolean array will be returned
containing the which samples have been selected.
"""
self._validate_estimator()
target_stats = Counter(y)
skf = _get_cv_splits(X, y, self.cv, self.random_state)
probabilities = np.zeros(y.shape[0], dtype=float)
for train_index, test_index in skf:
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
self.estimator_.fit(X_train, y_train)
probs = self.estimator_.predict_proba(X_test)
classes = self.estimator_.classes_
probabilities[test_index] = [
probs[l, np.where(classes == c)[0][0]]
for l, c in enumerate(y_test)
]
X_resampled = np.empty((0, X.shape[1]), dtype=X.dtype)
y_resampled = np.empty((0, ), dtype=y.dtype)
if self.return_indices:
idx_under = np.empty((0, ), dtype=int)
for target_class in np.unique(y):
if target_class in self.ratio_.keys():
n_samples = self.ratio_[target_class]
threshold = np.percentile(
probabilities[y == target_class],
(1. - (n_samples / target_stats[target_class])) * 100.)
index_target_class = np.flatnonzero(
probabilities[y == target_class] >= threshold)
else:
index_target_class = slice(None)
X_resampled = np.concatenate(
(X_resampled, X[y == target_class][index_target_class]),
axis=0)
y_resampled = np.concatenate(
(y_resampled, y[y == target_class][index_target_class]),
axis=0)
if self.return_indices:
idx_under = np.concatenate(
(idx_under, np.flatnonzero(y == target_class)[
index_target_class]), axis=0)
if self.return_indices:
return X_resampled, y_resampled, idx_under
else:
return X_resampled, y_resampled