[MRG] ENH: K-Means SMOTE implementation

doc/api.rst

@@ -71,6 +71,7 @@ Prototype selection
 
    over_sampling.ADASYN
    over_sampling.BorderlineSMOTE
+   over_sampling.KMeansSMOTE
    over_sampling.RandomOverSampler
    over_sampling.SMOTE
    over_sampling.SMOTENC

doc/over_sampling.rst

@@ -152,8 +152,8 @@ nearest neighbors class. Those variants are presented in the figure below.
    :align: center
 
 
-The :class:`BorderlineSMOTE` [HWB2005]_ and :class:`SVMSMOTE` [NCK2009]_ offer
-some variant of the SMOTE algorithm::
+The :class:`BorderlineSMOTE` [HWB2005]_, :class:`SVMSMOTE` [NCK2009]_, and
+:class:`KMeansSMOTE` [LDB2017]_ offer some variant of the SMOTE algorithm::
 
   >>> from imblearn.over_sampling import BorderlineSMOTE
   >>> X_resampled, y_resampled = BorderlineSMOTE().fit_resample(X, y)
@@ -209,6 +209,10 @@ other extra interpolation.
                Knowledge Engineering and Soft Data Paradigms, 3(1), pp.4-21,
                2009.
 
+  .. [LDB2017] Felix Last, Georgios Douzas, Fernando Bacao, "Oversampling for
+               Imbalanced Learning Based on K-Means and SMOTE"
+               https://arxiv.org/abs/1711.00837
+
 Mathematical formulation
 ========================
 
@@ -266,6 +270,10 @@ parameter of the SVM classifier allows to select more or less support vectors.
 For both borderline and SVM SMOTE, a neighborhood is defined using the
 parameter ``m_neighbors`` to decide if a sample is in danger, safe, or noise.
 
+**KMeans** SMOTE --- cf. to :class:`KMeansSMOTE` --- uses a KMeans clustering
+method before to apply SMOTE. The clustering will group samples together and
+generate new samples depending of the cluster density.
+
 ADASYN works similarly to the regular SMOTE. However, the number of
 samples generated for each :math:`x_i` is proportional to the number of samples
 which are not from the same class than :math:`x_i` in a given

doc/whats_new/v0.5.rst

@@ -37,6 +37,10 @@ Enhancement
   and issue template showing how to print system and dependency information
   from the command line. :pr:`557` by :user:`Alexander L. Hayes <batflyer>`.
 
+- Add :class:`imblearn.over_sampling.KMeansSMOTE` which is an over-sampler
+  clustering points before to apply SMOTE.
+  :pr:`435` by :user:`Stephan Heijl <StephanHeijl>`.
+
 Maintenance
 ...........
 

examples/over-sampling/plot_comparison_over_sampling.py

@@ -21,7 +21,8 @@
 
 from imblearn.pipeline import make_pipeline
 from imblearn.over_sampling import ADASYN
-from imblearn.over_sampling import SMOTE, BorderlineSMOTE, SVMSMOTE, SMOTENC
+from imblearn.over_sampling import (SMOTE, BorderlineSMOTE, SVMSMOTE, SMOTENC,
+                                    KMeansSMOTE)
 from imblearn.over_sampling import RandomOverSampler
 from imblearn.base import BaseSampler
 
@@ -204,18 +205,23 @@ def _fit_resample(self, X, y):
 # SMOTE proposes several variants by identifying specific samples to consider
 # during the resampling. The borderline version will detect which point to
 # select which are in the border between two classes. The SVM version will use
-# the support vectors found using an SVM algorithm to create new samples.
+# the support vectors found using an SVM algorithm to create new sample while
+# the KMeans version will make a clustering before to generate samples in each
+# cluster independently depending each cluster density.
 
 fig, ((ax1, ax2), (ax3, ax4),
-      (ax5, ax6), (ax7, ax8)) = plt.subplots(4, 2, figsize=(15, 30))
+      (ax5, ax6), (ax7, ax8),
+      (ax9, ax10)) = plt.subplots(5, 2, figsize=(15, 30))
 X, y = create_dataset(n_samples=5000, weights=(0.01, 0.05, 0.94),
                       class_sep=0.8)
 
-ax_arr = ((ax1, ax2), (ax3, ax4), (ax5, ax6), (ax7, ax8))
+
+ax_arr = ((ax1, ax2), (ax3, ax4), (ax5, ax6), (ax7, ax8), (ax9, ax10))
 for ax, sampler in zip(ax_arr,
                        (SMOTE(random_state=0),
                         BorderlineSMOTE(random_state=0, kind='borderline-1'),
                         BorderlineSMOTE(random_state=0, kind='borderline-2'),
+                        KMeansSMOTE(random_state=0),
                         SVMSMOTE(random_state=0))):
     clf = make_pipeline(sampler, LinearSVC())
     clf.fit(X, y)

imblearn/over_sampling/__init__.py

@@ -7,8 +7,9 @@
 from ._random_over_sampler import RandomOverSampler
 from ._smote import SMOTE
 from ._smote import BorderlineSMOTE
+from ._smote import KMeansSMOTE
 from ._smote import SVMSMOTE
 from ._smote import SMOTENC
 
-__all__ = ['ADASYN', 'RandomOverSampler',
+__all__ = ['ADASYN', 'RandomOverSampler', 'KMeansSMOTE',
            'SMOTE', 'BorderlineSMOTE', 'SVMSMOTE', 'SMOTENC']

imblearn/over_sampling/_smote.py

@@ -8,6 +8,7 @@
 
 from __future__ import division
 
+import math
 import types
 import warnings
 from collections import Counter
@@ -16,6 +17,8 @@
 from scipy import sparse
 
 from sklearn.base import clone
+from sklearn.cluster import MiniBatchKMeans
+from sklearn.metrics import pairwise_distances
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.svm import SVC
 from sklearn.utils import check_random_state
@@ -1090,3 +1093,236 @@ def _generate_sample(self, X, nn_data, nn_num, row, col, step):
             sample[start_idx + col_sel] = 1
 
         return sparse.csr_matrix(sample) if sparse.issparse(X) else sample
+
+
+@Substitution(
+    sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
+    random_state=_random_state_docstring)
+class KMeansSMOTE(BaseSMOTE):
+    """Apply a KMeans clustering before to over-sample using SMOTE.
+
+    This is an implementation of the algorithm described in [1]_.
+
+    Read more in the :ref:`User Guide <smote_adasyn>`.
+
+    Parameters
+    ----------
+    {sampling_strategy}
+
+    {random_state}
+
+    k_neighbors : int or object, optional (default=2)
+        If ``int``, number of nearest neighbours to used to construct synthetic
+        samples.  If object, an estimator that inherits from
+        :class:`sklearn.neighbors.base.KNeighborsMixin` that will be used to
+        find the k_neighbors.
+
+    n_jobs : int, optional (default=1)
+        The number of threads to open if possible.
+
+    kmeans_estimator : int or object, optional (default=MiniBatchKMeans())
+        A KMeans instance or the number of clusters to be used. By default,
+        we used a :class:`sklearn.cluster.MiniBatchKMeans` which tend to be
+        better with large number of samples.
+
+    cluster_balance_threshold : str or float, optional (default="auto")
+        The threshold at which a cluster is called balanced and where samples
+        of the class selected for SMOTE will be oversampled. If "auto", this
+        will be determined by the ratio for each class, or it can be set
+        manually.
+
+    density_exponent : str or float, optional (default="auto")
+        This exponent is used to determine the density of a cluster. Leaving
+        this to "auto" will use a feature-length based exponent.
+
+    Attributes
+    ----------
+    kmeans_estimator_ : estimator
+        The fitted clustering method used before to apply SMOTE.
+
+    nn_k_ : estimator
+        The fitted k-NN estimator used in SMOTE.
+
+    cluster_balance_threshold_ : float
+        The threshold used during ``fit`` for calling a cluster balanced.
+
+    References
+    ----------
+    .. [1] Felix Last, Georgios Douzas, Fernando Bacao, "Oversampling for
+       Imbalanced Learning Based on K-Means and SMOTE"
+       https://arxiv.org/abs/1711.00837
+
+    Examples
+    --------
+
+    >>> import numpy as np
+    >>> from imblearn.over_sampling import KMeansSMOTE
+    >>> from sklearn.datasets import make_blobs
+    >>> blobs = [100, 800, 100]
+    >>> X, y  = make_blobs(blobs, centers=[(-10, 0), (0,0), (10, 0)])
+    >>> # Add a single 0 sample in the middle blob
+    >>> X = np.concatenate([X, [[0, 0]]])
+    >>> y = np.append(y, 0)
+    >>> # Make this a binary classification problem
+    >>> y = y == 1
+    >>> sm = KMeansSMOTE(random_state=42)
+    >>> X_res, y_res = sm.fit_resample(X, y)
+    >>> # Find the number of new samples in the middle blob
+    >>> n_res_in_middle = ((X_res[:, 0] > -5) & (X_res[:, 0] < 5)).sum()
+    >>> print("Samples in the middle blob: %s" % n_res_in_middle)
+    Samples in the middle blob: 801
+    >>> print("Middle blob unchanged: %s" % (n_res_in_middle == blobs[1] + 1))
+    Middle blob unchanged: True
+    >>> print("More 0 samples: %s" % ((y_res == 0).sum() > (y == 0).sum()))
+    More 0 samples: True
+
+    """
+    def __init__(self,
+                 sampling_strategy='auto',
+                 random_state=None,
+                 k_neighbors=2,
+                 n_jobs=1,
+                 kmeans_estimator=None,
+                 cluster_balance_threshold="auto",
+                 density_exponent="auto"):
+        super().__init__(
+            sampling_strategy=sampling_strategy, random_state=random_state,
+            k_neighbors=k_neighbors, n_jobs=n_jobs)
+        self.kmeans_estimator = kmeans_estimator
+        self.cluster_balance_threshold = cluster_balance_threshold
+        self.density_exponent = density_exponent
+
+    def _validate_estimator(self):
+        super()._validate_estimator()
+        if self.kmeans_estimator is None:
+            self.kmeans_estimator_ = MiniBatchKMeans(
+                random_state=self.random_state)
+        elif isinstance(self.kmeans_estimator, int):
+            self.kmeans_estimator_ = MiniBatchKMeans(
+                n_clusters=self.kmeans_estimator,
+                random_state=self.random_state)
+        else:
+            self.kmeans_estimator_ = clone(self.kmeans_estimator)
+
+        # validate the parameters
+        for param_name in ('cluster_balance_threshold', 'density_exponent'):
+            param = getattr(self, param_name)
+            if isinstance(param, str) and param != 'auto':
+                raise ValueError(
+                    "'{}' should be 'auto' when a string is passed. "
+                    "Got {} instead.".format(param_name, repr(param))
+                )
+
+        self.cluster_balance_threshold_ = (
+            self.cluster_balance_threshold
+            if self.kmeans_estimator_.n_clusters != 1 else -np.inf
+        )
+
+
+    def _find_cluster_sparsity(self, X):
+        """Compute the cluster sparsity."""
+        euclidean_distances = pairwise_distances(X, metric="euclidean",
+                                                 n_jobs=self.n_jobs)
+        # negate diagonal elements
+        for ind in range(X.shape[0]):
+            euclidean_distances[ind, ind] = 0
+
+        non_diag_elements = (X.shape[0] ** 2) - X.shape[0]
+        mean_distance = euclidean_distances.sum() / non_diag_elements
+        exponent = (math.log(X.shape[0], 1.6) ** 1.8 * 0.16
+                    if self.density_exponent == 'auto'
+                    else self.density_exponent)
+        return (mean_distance ** exponent) / X.shape[0]
+
+    # FIXME: rename _sample -> _fit_resample in 0.6
+    def _fit_resample(self, X, y):
+        return self._sample(X, y)
+
+    def _sample(self, X, y):
+        self._validate_estimator()
+        X_resampled = X.copy()
+        y_resampled = y.copy()
+        total_inp_samples = sum(self.sampling_strategy_.values())
+
+        for class_sample, n_samples in self.sampling_strategy_.items():
+            if n_samples == 0:
+                continue
+
+            # target_class_indices = np.flatnonzero(y == class_sample)
+            # X_class = safe_indexing(X, target_class_indices)
+
+            X_clusters = self.kmeans_estimator_.fit_predict(X)
+            valid_clusters = []
+            cluster_sparsities = []
+
+            # identify cluster which are answering the requirements
+            for cluster_idx in range(self.kmeans_estimator_.n_clusters):
+
+                cluster_mask = np.flatnonzero(X_clusters == cluster_idx)
+                X_cluster = safe_indexing(X, cluster_mask)
+                y_cluster = safe_indexing(y, cluster_mask)
+
+                cluster_class_mean = (y_cluster == class_sample).mean()
+
+                if self.cluster_balance_threshold_ == "auto":
+                    balance_threshold = n_samples / total_inp_samples / 2
+                else:
+                    balance_threshold = self.cluster_balance_threshold_
+
+                # the cluster is already considered balanced
+                if cluster_class_mean < balance_threshold:
+                    continue
+
+                # not enough samples to apply SMOTE
+                anticipated_samples = cluster_class_mean * X_cluster.shape[0]
+                if anticipated_samples < self.nn_k_.n_neighbors:
+                    continue
+
+                X_cluster_class = safe_indexing(
+                    X_cluster, np.flatnonzero(y_cluster == class_sample)
+                )
+
+                valid_clusters.append(cluster_mask)
+                cluster_sparsities.append(
+                    self._find_cluster_sparsity(X_cluster_class)
+                )
+
+            cluster_sparsities = np.array(cluster_sparsities)
+            cluster_weights = cluster_sparsities / cluster_sparsities.sum()
+
+            if not valid_clusters:
+                raise RuntimeError(
+                    "No clusters found with sufficient samples of "
+                    "class {}. Try lowering the cluster_balance_threshold or "
+                    "or increasing the number of "
+                    "clusters.".format(class_sample))
+
+            for valid_cluster_idx, valid_cluster in enumerate(valid_clusters):
+                X_cluster = safe_indexing(X, valid_cluster)
+                y_cluster = safe_indexing(y, valid_cluster)
+
+                X_cluster_class = safe_indexing(
+                    X_cluster, np.flatnonzero(y_cluster == class_sample)
+                )
+
+                self.nn_k_.fit(X_cluster_class)
+                nns = self.nn_k_.kneighbors(X_cluster_class,
+                                            return_distance=False)[:, 1:]
+
+                cluster_n_samples = int(math.ceil(
+                    n_samples * cluster_weights[valid_cluster_idx])
+                )
+
+                X_new, y_new = self._make_samples(X_cluster_class,
+                                                  y.dtype,
+                                                  class_sample,
+                                                  X_cluster_class,
+                                                  nns,
+                                                  cluster_n_samples,
+                                                  1.0)
+
+                stack = [np.vstack, sparse.vstack][int(sparse.issparse(X_new))]
+                X_resampled = stack((X_resampled, X_new))
+                y_resampled = np.hstack((y_resampled, y_new))
+
+        return X_resampled, y_resampled