"""Class to perform under-sampling based on the condensed nearest neighbour
method."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
from __future__ import division
from collections import Counter
import numpy as np
from scipy.sparse import issparse
from sklearn.base import clone
from sklearn.neighbors import KNeighborsClassifier
from sklearn.utils import check_random_state, safe_indexing
from ..base import BaseCleaningSampler
from ...utils import Substitution
from ...utils.deprecation import deprecate_parameter
from ...utils._docstring import _random_state_docstring
[docs]@Substitution(
sampling_strategy=BaseCleaningSampler._sampling_strategy_docstring,
random_state=_random_state_docstring)
class CondensedNearestNeighbour(BaseCleaningSampler):
"""Class to perform under-sampling based on the condensed nearest neighbour
method.
Read more in the :ref:`User Guide <condensed_nearest_neighbors>`.
Parameters
----------
{sampling_strategy}
return_indices : bool, optional (default=False)
Whether or not to return the indices of the samples randomly
selected.
.. deprecated:: 0.4
``return_indices`` is deprecated. Use the attribute
``sample_indices_`` instead.
{random_state}
n_neighbors : int or object, optional (default=\
KNeighborsClassifier(n_neighbors=1))
If ``int``, size of the neighbourhood to consider to compute the
nearest neighbors. If object, an estimator that inherits from
:class:`sklearn.neighbors.base.KNeighborsMixin` that will be used to
find the nearest-neighbors.
n_seeds_S : int, optional (default=1)
Number of samples to extract in order to build the set S.
n_jobs : int, optional (default=1)
The number of threads to open if possible.
ratio : str, dict, or callable
.. deprecated:: 0.4
Use the parameter ``sampling_strategy`` instead. It will be removed
in 0.6.
Attributes
----------
sample_indices_ : ndarray, shape (n_new_samples)
Indices of the samples selected.
.. versionadded:: 0.4
``sample_indices_`` used instead of ``return_indices=True``.
Notes
-----
The method is based on [1]_.
Supports multi-class resampling. A one-vs.-rest scheme is used when
sampling a class as proposed in [1]_.
See also
--------
EditedNearestNeighbours, RepeatedEditedNearestNeighbours, AllKNN
References
----------
.. [1] P. Hart, "The condensed nearest neighbor rule,"
In Information Theory, IEEE Transactions on, vol. 14(3),
pp. 515-516, 1968.
Examples
--------
>>> from collections import Counter # doctest: +SKIP
>>> from sklearn.datasets import fetch_mldata # doctest: +SKIP
>>> from imblearn.under_sampling import \
CondensedNearestNeighbour # doctest: +SKIP
>>> pima = fetch_mldata('diabetes_scale') # doctest: +SKIP
>>> X, y = pima['data'], pima['target'] # doctest: +SKIP
>>> print('Original dataset shape %s' % Counter(y)) # doctest: +SKIP
Original dataset shape Counter({{1: 500, -1: 268}}) # doctest: +SKIP
>>> cnn = CondensedNearestNeighbour(random_state=42) # doctest: +SKIP
>>> X_res, y_res = cnn.fit_resample(X, y) #doctest: +SKIP
>>> print('Resampled dataset shape %s' % Counter(y_res)) # doctest: +SKIP
Resampled dataset shape Counter({{-1: 268, 1: 227}}) # doctest: +SKIP
"""
[docs] def __init__(self,
sampling_strategy='auto',
return_indices=False,
random_state=None,
n_neighbors=None,
n_seeds_S=1,
n_jobs=1,
ratio=None):
super(CondensedNearestNeighbour, self).__init__(
sampling_strategy=sampling_strategy, ratio=ratio)
self.random_state = random_state
self.return_indices = return_indices
self.n_neighbors = n_neighbors
self.n_seeds_S = n_seeds_S
self.n_jobs = n_jobs
def _validate_estimator(self):
"""Private function to create the NN estimator"""
if self.n_neighbors is None:
self.estimator_ = KNeighborsClassifier(
n_neighbors=1, n_jobs=self.n_jobs)
elif isinstance(self.n_neighbors, int):
self.estimator_ = KNeighborsClassifier(
n_neighbors=self.n_neighbors, n_jobs=self.n_jobs)
elif isinstance(self.n_neighbors, KNeighborsClassifier):
self.estimator_ = clone(self.n_neighbors)
else:
raise ValueError('`n_neighbors` has to be a int or an object'
' inhereited from KNeighborsClassifier.'
' Got {} instead.'.format(type(self.n_neighbors)))
def _fit_resample(self, X, y):
if self.return_indices:
deprecate_parameter(self, '0.4', 'return_indices',
'sample_indices_')
self._validate_estimator()
random_state = check_random_state(self.random_state)
target_stats = Counter(y)
class_minority = min(target_stats, key=target_stats.get)
idx_under = np.empty((0, ), dtype=int)
for target_class in np.unique(y):
if target_class in self.sampling_strategy_.keys():
# Randomly get one sample from the majority class
# Generate the index to select
idx_maj = np.flatnonzero(y == target_class)
idx_maj_sample = idx_maj[random_state.randint(
low=0,
high=target_stats[target_class],
size=self.n_seeds_S)]
# Create the set C - One majority samples and all minority
C_indices = np.append(
np.flatnonzero(y == class_minority), idx_maj_sample)
C_x = safe_indexing(X, C_indices)
C_y = safe_indexing(y, C_indices)
# Create the set S - all majority samples
S_indices = np.flatnonzero(y == target_class)
S_x = safe_indexing(X, S_indices)
S_y = safe_indexing(y, S_indices)
# fit knn on C
self.estimator_.fit(C_x, C_y)
good_classif_label = idx_maj_sample.copy()
# Check each sample in S if we keep it or drop it
for idx_sam, (x_sam, y_sam) in enumerate(zip(S_x, S_y)):
# Do not select sample which are already well classified
if idx_sam in good_classif_label:
continue
# Classify on S
if not issparse(x_sam):
x_sam = x_sam.reshape(1, -1)
pred_y = self.estimator_.predict(x_sam)
# If the prediction do not agree with the true label
# append it in C_x
if y_sam != pred_y:
# Keep the index for later
idx_maj_sample = np.append(idx_maj_sample,
idx_maj[idx_sam])
# Update C
C_indices = np.append(C_indices, idx_maj[idx_sam])
C_x = safe_indexing(X, C_indices)
C_y = safe_indexing(y, C_indices)
# fit a knn on C
self.estimator_.fit(C_x, C_y)
# This experimental to speed up the search
# Classify all the element in S and avoid to test the
# well classified elements
pred_S_y = self.estimator_.predict(S_x)
good_classif_label = np.unique(
np.append(idx_maj_sample,
np.flatnonzero(pred_S_y == S_y)))
idx_under = np.concatenate((idx_under, idx_maj_sample), axis=0)
else:
idx_under = np.concatenate(
(idx_under, np.flatnonzero(y == target_class)), axis=0)
self.sample_indices_ = idx_under
if self.return_indices:
return (safe_indexing(X, idx_under), safe_indexing(y, idx_under),
idx_under)
return safe_indexing(X, idx_under), safe_indexing(y, idx_under)