[MRG] ENH: safe-level SMOTE

imblearn/over_sampling/__init__.py

@@ -10,6 +10,7 @@
 from ._smote import KMeansSMOTE
 from ._smote import SVMSMOTE
 from ._smote import SMOTENC
+from ._smote import SLSMOTE
 
 __all__ = [
     "ADASYN",
@@ -19,4 +20,5 @@
     "BorderlineSMOTE",
     "SVMSMOTE",
     "SMOTENC",
+    "SLSMOTE",
 ]

imblearn/over_sampling/_smote.py

@@ -586,12 +586,14 @@ def _fit_resample(self, X, y):
             n_generated_samples = int(fractions * (n_samples + 1))
             if np.count_nonzero(danger_bool) > 0:
                 nns = self.nn_k_.kneighbors(
-                    _safe_indexing(support_vector, np.flatnonzero(danger_bool)),
+                    _safe_indexing(
+                        support_vector, np.flatnonzero(danger_bool)),
                     return_distance=False,
                 )[:, 1:]
 
                 X_new_1, y_new_1 = self._make_samples(
-                    _safe_indexing(support_vector, np.flatnonzero(danger_bool)),
+                    _safe_indexing(
+                        support_vector, np.flatnonzero(danger_bool)),
                     y.dtype,
                     class_sample,
                     X_class,
@@ -602,12 +604,14 @@ def _fit_resample(self, X, y):
 
             if np.count_nonzero(safety_bool) > 0:
                 nns = self.nn_k_.kneighbors(
-                    _safe_indexing(support_vector, np.flatnonzero(safety_bool)),
+                    _safe_indexing(
+                        support_vector, np.flatnonzero(safety_bool)),
                     return_distance=False,
                 )[:, 1:]
 
                 X_new_2, y_new_2 = self._make_samples(
-                    _safe_indexing(support_vector, np.flatnonzero(safety_bool)),
+                    _safe_indexing(
+                        support_vector, np.flatnonzero(safety_bool)),
                     y.dtype,
                     class_sample,
                     X_class,
@@ -1308,3 +1312,319 @@ def _fit_resample(self, X, y):
                 y_resampled = np.hstack((y_resampled, y_new))
 
         return X_resampled, y_resampled
+
+
+@Substitution(
+    sampling_strategy=BaseOverSampler._sampling_strategy_docstring,
+    random_state=_random_state_docstring,
+)
+class SLSMOTE(BaseSMOTE):
+    """Class to perform over-sampling using safe-level SMOTE.
+    This is an implementation of the Safe-level-SMOTE described in [2]_.
+
+    Parameters
+    -----------
+    {sampling_strategy}
+
+    {random_state}
+
+    k_neighbors : int or object, optional (default=5)
+        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.
+
+    m_neighbors : int or object, optional (default=10)
+        If ``int``, number of nearest neighbours used to determine the safe
+        level of an instance. If object, an estimator that inherits from
+        :class:`sklearn.neighbors.base.KNeighborsMixin` that will be used
+        to find the m_neighbors.
+
+    n_jobs : int or None, optional (default=None)
+        Number of CPU cores used during the cross-validation loop.
+        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
+        ``-1`` means using all processors. See
+        `Glossary <https://scikit-learn.org/stable/glossary.html#term-n-jobs>`_
+        for more details.
+
+
+    Notes
+    -----
+    See the original papers: [2]_ for more details.
+
+    Supports multi-class resampling. A one-vs.-rest scheme is used as
+    originally proposed in [1]_.
+
+    See also
+    --------
+    SMOTE : Over-sample using SMOTE.
+
+    SMOTENC : Over-sample using SMOTE for continuous and categorical features.
+
+    SVMSMOTE : Over-sample using SVM-SMOTE variant.
+
+    BorderlineSMOTE : Over-sample using Borderline-SMOTE.
+
+    ADASYN : Over-sample using ADASYN.
+
+    KMeansSMOTE: Over-sample using KMeans-SMOTE variant.
+
+    References
+    ----------
+    .. [1] N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, "SMOTE:
+       synthetic minority over-sampling technique," Journal of artificial
+       intelligence research, 321-357, 2002.
+
+    .. [2] C. Bunkhumpornpat, K. Sinapiromsaran, C. Lursinsap, "Safe-level-
+       SMOTE: Safe-level-synthetic minority over-sampling technique for
+       handling the class imbalanced problem," In: Theeramunkong T.,
+       Kijsirikul B., Cercone N., Ho TB. (eds) Advances in Knowledge Discovery
+       and Data Mining. PAKDD 2009. Lecture Notes in Computer Science,
+       vol 5476. Springer, Berlin, Heidelberg, 475-482, 2009.
+
+
+     Examples
+    --------
+
+    >>> from collections import Counter
+    >>> from sklearn.datasets import make_classification
+    >>> from imblearn.over_sampling import \
+SLSMOTE # doctest: +NORMALIZE_WHITESPACE
+    >>> 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 %s' % Counter(y))
+    Original dataset shape Counter({{1: 900, 0: 100}})
+    >>> sm = SLSMOTE(random_state=42)
+    >>> X_res, y_res = sm.fit_resample(X, y)
+    >>> print('Resampled dataset shape %s' % Counter(y_res))
+    Resampled dataset shape Counter({{0: 900, 1: 900}})
+
+    """
+
+    def __init__(self,
+                 sampling_strategy='auto',
+                 random_state=None,
+                 k_neighbors=5,
+                 m_neighbors=10,
+                 n_jobs=None):
+
+        super().__init__(sampling_strategy=sampling_strategy,
+                         random_state=random_state, k_neighbors=k_neighbors,
+                         n_jobs=n_jobs)
+
+        self.m_neighbors = m_neighbors
+
+    def _assign_sl(self, nn_estimator, samples, target_class, y):
+        '''
+        Assign the safe levels to the instances in the target class.
+
+        Parameters
+        ----------
+        nn_estimator : estimator
+            An estimator that inherits from
+            :class:`sklearn.neighbors.base.KNeighborsMixin`. It gets the
+            nearest neighbors that are used to determine the safe levels.
+
+        samples : {array-like, sparse matrix}, shape (n_samples, n_features)
+            The samples to which the safe levels are assigned.
+
+        target_class : int or str
+            The target corresponding class being over-sampled.
+
+        y : array-like, shape (n_samples,)
+            The true label in order to calculate the safe levels.
+
+        Returns
+        -------
+        output : ndarray, shape (n_samples,)
+            A ndarray where the values refer to the safe level of the
+            instances in the target class.
+        '''
+
+        x = nn_estimator.kneighbors(samples, return_distance=False)[:, 1:]
+        nn_label = (y[x] == target_class).astype(int)
+        sl = np.sum(nn_label, axis=1)
+        return sl
+
+    def _validate_estimator(self):
+        super()._validate_estimator()
+        self.nn_m_ = check_neighbors_object('m_neighbors', self.m_neighbors,
+                                            additional_neighbor=1)
+        self.nn_m_.set_params(**{"n_jobs": self.n_jobs})
+
+    def _fit_resample(self, X, y):
+        self._validate_estimator()
+
+        X_resampled = X.copy()
+        y_resampled = y.copy()
+
+        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)
+
+            self.nn_m_.fit(X)
+            sl = self._assign_sl(self.nn_m_, X_class, class_sample, y)
+
+            # filter the points in X_class that have safe level >0
+            # If safe level = 0, the point is not used to
+            # generate synthetic instances
+            X_safe_indices = np.flatnonzero(sl != 0)
+            X_safe_class = _safe_indexing(X_class, X_safe_indices)
+
+            self.nn_k_.fit(X_class)
+            nns = self.nn_k_.kneighbors(X_safe_class,
+                                        return_distance=False)[:, 1:]
+
+            sl_safe_class = sl[X_safe_indices]
+            sl_nns = sl[nns]
+            sl_safe_t = np.array([sl_safe_class]).transpose()
+            with np.errstate(divide='ignore'):
+                sl_ratio = np.divide(sl_safe_t, sl_nns)
+
+            X_new, y_new = self._make_samples_sl(X_safe_class, y.dtype,
+                                                 class_sample, X_class,
+                                                 nns, n_samples, sl_ratio,
+                                                 1.0)
+
+            if sparse.issparse(X_new):
+                X_resampled = sparse.vstack([X_resampled, X_new])
+            else:
+                X_resampled = np.vstack((X_resampled, X_new))
+            y_resampled = np.hstack((y_resampled, y_new))
+
+        return X_resampled, y_resampled
+
+    def _make_samples_sl(self, X, y_dtype, y_type, nn_data, nn_num,
+                         n_samples, sl_ratio, step_size=1.):
+        """A support function that returns artificial samples using
+        safe-level SMOTE. It is similar to _make_samples method for SMOTE.
+
+         Parameters
+        ----------
+        X : {array-like, sparse matrix}, shape (n_samples_safe, n_features)
+            Points from which the points will be created.
+
+        y_dtype : dtype
+            The data type of the targets.
+
+        y_type : str or int
+            The minority target value, just so the function can return the
+            target values for the synthetic variables with correct length in
+            a clear format.
+
+        nn_data : ndarray, shape (n_samples_all, n_features)
+            Data set carrying all the neighbours to be used
+
+        nn_num : ndarray, shape (n_samples_safe, k_nearest_neighbours)
+            The nearest neighbours of each sample in `nn_data`.
+
+        n_samples : int
+            The number of samples to generate.
+
+        sl_ratio: ndarray, shape (n_samples_safe, k_nearest_neighbours)
+
+        step_size : float, optional (default=1.)
+            The step size to create samples.
+
+
+        Returns
+        -------
+        X_new : {ndarray, sparse matrix}, shape (n_samples_new, n_features)
+            Synthetically generated samples using the safe-level method.
+
+        y_new : ndarray, shape (n_samples_new,)
+            Target values for synthetic samples.
+
+        """
+
+        random_state = check_random_state(self.random_state)
+        samples_indices = random_state.randint(low=0,
+                                               high=len(nn_num.flatten()),
+                                               size=n_samples)
+        rows = np.floor_divide(samples_indices, nn_num.shape[1])
+        cols = np.mod(samples_indices, nn_num.shape[1])
+        gap_arr = step_size * self._vgenerate_gap(sl_ratio)
+        gaps = gap_arr.flatten()[samples_indices]
+
+        y_new = np.array([y_type] * n_samples, dtype=y_dtype)
+
+        if sparse.issparse(X):
+            row_indices, col_indices, samples = [], [], []
+            for i, (row, col, gap) in enumerate(zip(rows, cols, gaps)):
+                if X[row].nnz:
+                    sample = self._generate_sample(
+                        X, nn_data, nn_num, row, col, gap)
+                    row_indices += [i] * len(sample.indices)
+                    col_indices += sample.indices.tolist()
+                    samples += sample.data.tolist()
+            return (
+                sparse.csr_matrix(
+                    (samples, (row_indices, col_indices)),
+                    [len(samples_indices), X.shape[1]],
+                    dtype=X.dtype,
+                ),
+                y_new,
+            )
+
+        else:
+            X_new = np.zeros((n_samples, X.shape[1]), dtype=X.dtype)
+            for i, (row, col, gap) in enumerate(zip(rows, cols, gaps)):
+                X_new[i] = self._generate_sample(X, nn_data, nn_num,
+                                                 row, col, gap)
+
+        return X_new, y_new
+
+    def _generate_gap(self, a_ratio, rand_state=None):
+        """ generate gap according to sl_ratio, non-vectorized version.
+
+        Parameters
+        ----------
+        a_ratio: float
+                 sl_ratio of a single data point
+
+        rand_state: random state object or int
+
+
+        Returns
+        ------------
+        gap: float
+             a number between 0 and 1
+
+        """
+
+        random_state = check_random_state(rand_state)
+        if np.isinf(a_ratio):
+            gap = 0
+        elif a_ratio >= 1:
+            gap = random_state.uniform(0, 1/a_ratio)
+        elif 0 < a_ratio < 1:
+            gap = random_state.uniform(1-a_ratio, 1)
+        else:
+            raise ValueError('sl_ratio should be nonegative')
+        return gap
+
+    def _vgenerate_gap(self, sl_ratio):
+        """
+        generate gap according to sl_ratio, vectorized version of _generate_gap
+
+        Parameters
+        -----------
+        sl_ratio: ndarray  shape (n_samples_safe, k_nearest_neighbours)
+                  sl_ratio of all instances with safe_level>0 in the specified
+                  class
+
+        Returns
+        ------------
+        gap_arr: ndarray  shape (n_samples_safe, k_nearest_neighbours)
+                 the gap for all instances with safe_level>0 in the specified
+                 class
+
+        """
+        prng = check_random_state(self.random_state)
+        rand_state = prng.randint(sl_ratio.size+1, size=sl_ratio.shape)
+        vgap = np.vectorize(self._generate_gap)
+        gap_arr = vgap(sl_ratio, rand_state)
+        return gap_arr