added AllKNN under-sampling method

README.rst

@@ -106,6 +106,7 @@ Below is a list of the methods currently implemented in this module.
     8. Edited Nearest Neighbours [6]_
     9. Instance Hardness Threshold [7]_
     10. Repeated Edited Nearest Neighbours [14]_
+    11. AllKNN [14]_
 
 * Over-sampling
     1. Random minority over-sampling with replacement

doc/todo.rst

@@ -12,9 +12,9 @@ Version 0.2
 New methods
 ~~~~~~~~~~~
 
-* AIIKNN_: Garcia, Salvador, et al. "Prototype selection for nearest neighbor classification: Taxonomy and empirical study." IEEE Transactions on Pattern Analysis and Machine Intelligence 34.3 (2012): 417-435.
+* SMOTEBagging_: Wang, Shuo, and Xin Yao. "Diversity analysis on imbalanced data sets by using ensemble models." Computational Intelligence and Data Mining, 2009. CIDM'09. IEEE Symposium on. IEEE, 2009.
 
-.. _AIIKNN: https://www.semanticscholar.org/paper/Prototype-Selection-for-Nearest-Neighbor-Garc%C3%ADa-Derrac/fbca1824c49e02da37e5e780eaf0ab6ddfaf5614/pdf
+.. _SMOTEBagging: http://pages.bangor.ac.uk/~mas00a/papers/jpjrcolkis15.pdf
 
 API improvements
 ~~~~~~~~~~~~~~~~

doc/whats_new.rst

@@ -14,6 +14,7 @@ Changelog
 
 - Added support for bumpversion.
 - Added doctest in the documentation.
+- Added AllKNN under sampling technique.
 
 
 .. _changes_0_1:

examples/under-sampling/plot_allknn.py

@@ -0,0 +1,92 @@
+"""
+==================================
+AllKNN
+==================================
+
+An illustration of the AllKNN method.
+
+"""
+
+print(__doc__)
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+sns.set()
+
+# Define some color for the plotting
+almost_black = '#262626'
+palette = sns.color_palette()
+
+from sklearn.datasets import make_classification
+from sklearn.decomposition import PCA
+
+from imblearn.under_sampling import EditedNearestNeighbours
+from imblearn.under_sampling import RepeatedEditedNearestNeighbours
+from imblearn.under_sampling import AllKNN
+
+# Generate the dataset
+X, y = make_classification(n_classes=2, class_sep=1.25, weights=[0.3, 0.7],
+                           n_informative=3, n_redundant=1, flip_y=0,
+                           n_features=5, n_clusters_per_class=1,
+                           n_samples=5000, random_state=10)
+
+# Instanciate a PCA object for the sake of easy visualisation
+pca = PCA(n_components=2)
+# Fit and transform x to visualise inside a 2D feature space
+X_vis = pca.fit_transform(X)
+
+# Three subplots, unpack the axes array immediately
+f, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4)
+
+ax1.scatter(X_vis[y == 0, 0], X_vis[y == 0, 1], label="Class #0", alpha=.5,
+            edgecolor=almost_black, facecolor=palette[0], linewidth=0.15)
+ax1.scatter(X_vis[y == 1, 0], X_vis[y == 1, 1], label="Class #1", alpha=.5,
+            edgecolor=almost_black, facecolor=palette[2], linewidth=0.15)
+ax1.set_title('Original set')
+
+# Apply the ENN
+print('ENN')
+enn = EditedNearestNeighbours()
+X_resampled, y_resampled = enn.fit_sample(X, y)
+X_res_vis = pca.transform(X_resampled)
+print('Reduced {:.2f}\%'.format(100 * (1 - float(len(X_resampled))/ len(X))))
+
+ax2.scatter(X_res_vis[y_resampled == 0, 0], X_res_vis[y_resampled == 0, 1],
+            label="Class #0", alpha=.5, edgecolor=almost_black,
+            facecolor=palette[0], linewidth=0.15)
+ax2.scatter(X_res_vis[y_resampled == 1, 0], X_res_vis[y_resampled == 1, 1],
+            label="Class #1", alpha=.5, edgecolor=almost_black,
+            facecolor=palette[2], linewidth=0.15)
+ax2.set_title('Edited nearest neighbours')
+
+# Apply the RENN
+print('RENN')
+renn = RepeatedEditedNearestNeighbours()
+X_resampled, y_resampled = renn.fit_sample(X, y)
+X_res_vis = pca.transform(X_resampled)
+print('Reduced {:.2f}\%'.format(100 * (1 - float(len(X_resampled))/ len(X))))
+
+ax3.scatter(X_res_vis[y_resampled == 0, 0], X_res_vis[y_resampled == 0, 1],
+            label="Class #0", alpha=.5, edgecolor=almost_black,
+            facecolor=palette[0], linewidth=0.15)
+ax3.scatter(X_res_vis[y_resampled == 1, 0], X_res_vis[y_resampled == 1, 1],
+            label="Class #1", alpha=.5, edgecolor=almost_black,
+            facecolor=palette[2], linewidth=0.15)
+ax3.set_title('Repeated Edited nearest neighbours')
+
+# Apply the AllKNN
+print('AllKNN')
+allknn = AllKNN()
+X_resampled, y_resampled = allknn.fit_sample(X, y)
+X_res_vis = pca.transform(X_resampled)
+print('Reduced {:.2f}\%'.format(100 * (1 - float(len(X_resampled))/ len(X))))
+
+ax4.scatter(X_res_vis[y_resampled == 0, 0], X_res_vis[y_resampled == 0, 1],
+            label="Class #0", alpha=.5, edgecolor=almost_black,
+            facecolor=palette[0], linewidth=0.15)
+ax4.scatter(X_res_vis[y_resampled == 1, 0], X_res_vis[y_resampled == 1, 1],
+            label="Class #1", alpha=.5, edgecolor=almost_black,
+            facecolor=palette[2], linewidth=0.15)
+ax4.set_title('AllKNN')
+
+plt.show()

imblearn/under_sampling/__init__.py

@@ -12,6 +12,7 @@
 from .neighbourhood_cleaning_rule import NeighbourhoodCleaningRule
 from .edited_nearest_neighbours import EditedNearestNeighbours
 from .edited_nearest_neighbours import RepeatedEditedNearestNeighbours
+from .edited_nearest_neighbours import AllKNN
 from .instance_hardness_threshold import InstanceHardnessThreshold
 
 __all__ = ['RandomUnderSampler',
@@ -23,4 +24,5 @@
            'NeighbourhoodCleaningRule',
            'EditedNearestNeighbours',
            'RepeatedEditedNearestNeighbours',
+           'AllKNN',
            'InstanceHardnessThreshold']

imblearn/under_sampling/edited_nearest_neighbours.py

@@ -382,3 +382,173 @@ def _sample(self, X, y):
             return X_resampled, y_resampled, idx_under
         else:
             return X_resampled, y_resampled
+
+
+class AllKNN(SamplerMixin):
+    """Class to perform under-sampling based on the AllKNN method.
+
+    Parameters
+    ----------
+    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.
+
+    size_ngh : int, optional (default=3)
+        Size of the neighbourhood to consider to compute the average
+        distance to the minority point samples.
+
+    kind_sel : str, optional (default='all')
+        Strategy to use in order to exclude samples.
+
+        - If 'all', all neighbours will have to agree with the samples of
+        interest to not be excluded.
+        - If 'mode', the majority vote of the neighbours will be used in
+        order to exclude a sample.
+
+    n_jobs : int, optional (default=-1)
+        The number of thread to open when it is possible.
+
+    Attributes
+    ----------
+    min_c_ : str or int
+        The identifier of the minority class.
+
+    max_c_ : str or int
+        The identifier of the majority class.
+
+    stats_c_ : dict of str/int : int
+        A dictionary in which the number of occurences of each class is
+        reported.
+
+    X_shape_ : tuple of int
+        Shape of the data `X` during fitting.
+
+    Notes
+    -----
+    The method is based on [1]_.
+
+    This class supports multi-class.
+
+    Examples
+    --------
+
+    >>> from collections import Counter
+    >>> from sklearn.datasets import make_classification
+    >>> from imblearn.under_sampling import AllKNN
+    >>> 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})
+    >>> allknn = AllKNN(random_state=42)
+    >>> X_res, y_res = allknn.fit_sample(X, y)
+    >>> print('Resampled dataset shape {}'.format(Counter(y_res)))
+    Resampled dataset shape Counter({1: 883, 0: 100})
+
+    References
+    ----------
+    .. [1] I. Tomek, "An Experiment with the Edited Nearest-Neighbor
+       Rule," IEEE Transactions on Systems, Man, and Cybernetics, vol. 6(6),
+       pp. 448-452, June 1976.
+
+    """
+
+    def __init__(self, return_indices=False, random_state=None,
+                 size_ngh=3, kind_sel='all', n_jobs=-1):
+        super(AllKNN, self).__init__()
+        self.return_indices = return_indices
+        self.random_state = random_state
+        self.size_ngh = size_ngh
+        self.kind_sel = kind_sel
+        self.n_jobs = n_jobs
+        self.enn_ = EditedNearestNeighbours(
+            return_indices=self.return_indices,
+            random_state=self.random_state,
+            size_ngh=self.size_ngh,
+            kind_sel=self.kind_sel,
+            n_jobs=self.n_jobs)
+
+    def fit(self, X, y):
+        """Find the classes statistics before to perform sampling.
+
+        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
+        -------
+        self : object,
+            Return self.
+
+        """
+        super(AllKNN, self).fit(X, y)
+        self.enn_.fit(X, y)
+
+        return self
+
+    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.
+
+        """
+
+        if self.kind_sel not in SEL_KIND:
+            raise NotImplementedError
+
+        X_, y_ = X, y
+
+        if self.return_indices:
+            idx_under = np.arange(X.shape[0], dtype=int)
+
+        prev_len = y.shape[0]
+
+        for curr_size_ngh in range(1, self.size_ngh + 1):
+            self.logger.debug('Apply ENN size_ngh #%s', curr_size_ngh)
+            # updating ENN size_ngh
+            self.enn_.size_ngh = curr_size_ngh
+            if self.return_indices:
+                X_, y_, idx_ = self.enn_.fit_sample(X_, y_)
+                idx_under = idx_under[idx_]
+            else:
+                X_, y_ = self.enn_.fit_sample(X_, y_)
+
+        self.logger.info('Under-sampling performed: %s', Counter(y_))
+
+        X_resampled, y_resampled = X_, y_
+
+        # Check if the indices of the samples selected should be returned too
+        if self.return_indices:
+            # Return the indices of interest
+            return X_resampled, y_resampled, idx_under
+        else:
+            return X_resampled, y_resampled