From dae3ba387dc8b04373a59a6326e5fbd24f983250 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Thu, 1 Mar 2018 02:03:46 +0100
Subject: [PATCH 1/9] EHN accept one-vs-all targets

---
 imblearn/__init__.py         |  3 +++
 imblearn/base.py             | 19 ++++++++++++++++---
 imblearn/utils/validation.py | 28 +++++++++++++++++++++-------
 3 files changed, 40 insertions(+), 10 deletions(-)

diff --git a/imblearn/__init__.py b/imblearn/__init__.py
index 9f05adb1f..7803ca016 100644
--- a/imblearn/__init__.py
+++ b/imblearn/__init__.py
@@ -13,6 +13,9 @@
 exceptions
     Module including custom warnings and error clases used across
     imbalanced-learn.
+keras
+    Module which provides custom generator, layers for deep learning using
+    keras.
 metrics
     Module which provides metrics to quantified the classification performance
     with imbalanced dataset.
diff --git a/imblearn/base.py b/imblearn/base.py
index aa12eb365..d352d9696 100644
--- a/imblearn/base.py
+++ b/imblearn/base.py
@@ -9,9 +9,13 @@
 import logging
 from abc import ABCMeta, abstractmethod
 
+import numpy as np
+
 from sklearn.base import BaseEstimator
 from sklearn.externals import six
+from sklearn.preprocessing import label_binarize
 from sklearn.utils import check_X_y
+from sklearn.utils.multiclass import type_of_target
 from sklearn.utils.validation import check_is_fitted
 
 from .utils import check_ratio, check_target_type, hash_X_y
@@ -54,14 +58,23 @@ def sample(self, X, y):
             The corresponding label of `X_resampled`
 
         """
-
         # Check the consistency of X and y
+        y, binarize_y = check_target_type(y, indicate_one_vs_all=True)
         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
 
         check_is_fitted(self, 'ratio_')
         self._check_X_y(X, y)
 
-        return self._sample(X, y)
+        output = self._sample(X, y)
+
+        if binarize_y:
+            y_sampled = label_binarize(output[1], np.unique(y))
+            if len(output) == 2:
+                return output[0], y_sampled
+            else:
+                return output[0], y_sampled, output[2]
+        else:
+            return output
 
     def fit_sample(self, X, y):
         """Fit the statistics and resample the data directly.
@@ -152,8 +165,8 @@ def fit(self, X, y):
             Return self.
 
         """
-        X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
         y = check_target_type(y)
+        X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
         self.X_hash_, self.y_hash_ = hash_X_y(X, y)
         # self.sampling_type is already checked in check_ratio
         self.ratio_ = check_ratio(self.ratio, y, self._sampling_type)
diff --git a/imblearn/utils/validation.py b/imblearn/utils/validation.py
index d009dacbe..17fa7e93a 100644
--- a/imblearn/utils/validation.py
+++ b/imblearn/utils/validation.py
@@ -10,6 +10,7 @@
 
 import numpy as np
 
+from sklearn.preprocessing import label_binarize
 from sklearn.neighbors.base import KNeighborsMixin
 from sklearn.neighbors import NearestNeighbors
 from sklearn.externals import six, joblib
@@ -19,7 +20,7 @@
 
 SAMPLING_KIND = ('over-sampling', 'under-sampling', 'clean-sampling',
                  'ensemble')
-TARGET_KIND = ('binary', 'multiclass')
+TARGET_KIND = ('binary', 'multiclass', 'multilabel-indicator')
 
 
 def check_neighbors_object(nn_name, nn_object, additional_neighbor=0):
@@ -54,29 +55,42 @@ def check_neighbors_object(nn_name, nn_object, additional_neighbor=0):
         raise_isinstance_error(nn_name, [int, KNeighborsMixin], nn_object)
 
 
-def check_target_type(y):
+def check_target_type(y, indicate_one_vs_all=False):
     """Check the target types to be conform to the current samplers.
 
-    The current samplers should be compatible with ``'binary'`` and
-    ``'multiclass'`` targets only.
+    The current samplers should be compatible with ``'binary'``,
+    ``'multilabel-indicator'`` and ``'multiclass'`` targets only.
 
     Parameters
     ----------
     y : ndarray,
-        The array containing the target
+        The array containing the target.
+
+    indicate_one_vs_all : bool, optional
+        Either to indicate if the targets are encoded in a one-vs-all fashion.
 
     Returns
     -------
     y : ndarray,
         The returned target.
 
+    is_one_vs_all : bool, optional
+        Indicate if the target was originally encoded in a one-vs-all fashion.
+        Only returned if ``indicate_multilabel=True``.
+
     """
-    if type_of_target(y) not in TARGET_KIND:
+    type_y = type_of_target(y)
+    if type_y not in TARGET_KIND:
         # FIXME: perfectly we should raise an error but the sklearn API does
         # not allow for it
         warnings.warn("'y' should be of types {} only. Got {} instead.".format(
             TARGET_KIND, type_of_target(y)))
-    return y
+
+    if indicate_one_vs_all:
+        return (y.argmax(axis=1) if type_y == 'multilabel-indicator' else y,
+                type_y == 'multilabel-indicator')
+    else:
+        return y.argmax(axis=1) if type_y == 'multilabel-indicator' else y
 
 
 def hash_X_y(X, y, n_samples=10, n_features=5):

From 7487ce48d7037823af4253064e8a7460b9594c50 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Thu, 1 Mar 2018 02:29:49 +0100
Subject: [PATCH 2/9] TST add test for check_target_type

---
 imblearn/utils/tests/test_validation.py | 32 +++++++++++++++++++++++++
 imblearn/utils/validation.py            |  1 -
 2 files changed, 32 insertions(+), 1 deletion(-)

diff --git a/imblearn/utils/tests/test_validation.py b/imblearn/utils/tests/test_validation.py
index a1d8585df..49e9d6997 100644
--- a/imblearn/utils/tests/test_validation.py
+++ b/imblearn/utils/tests/test_validation.py
@@ -6,17 +6,20 @@
 from collections import Counter
 
 import numpy as np
+import pytest
 from pytest import raises
 
 from sklearn.neighbors.base import KNeighborsMixin
 from sklearn.neighbors import NearestNeighbors
 from sklearn.utils import check_random_state
 from sklearn.externals import joblib
+from sklearn.utils.testing import assert_array_equal
 
 from imblearn.utils.testing import warns
 from imblearn.utils import check_neighbors_object
 from imblearn.utils import check_ratio
 from imblearn.utils import hash_X_y
+from imblearn.utils import check_target_type
 
 
 def test_check_neighbors_object():
@@ -35,6 +38,35 @@ def test_check_neighbors_object():
         check_neighbors_object(name, n_neighbors)
 
 
+@pytest.mark.parametrize(
+    "target, output_target",
+    [(np.array([0, 1, 1]), np.array([0, 1, 1])),
+     (np.array([0, 1, 2]), np.array([0, 1, 2])),
+     (np.array([[0, 1], [1, 0]]), np.array([1, 0]))]
+)
+def test_check_target_type(target, output_target):
+    converted_target = check_target_type(target.astype(int))
+    assert_array_equal(converted_target, output_target.astype(int))
+
+
+@pytest.mark.parametrize(
+    "target, output_target, is_ova",
+    [(np.array([0, 1, 1]), np.array([0, 1, 1]), False),
+     (np.array([0, 1, 2]), np.array([0, 1, 2]), False),
+     (np.array([[0, 1], [1, 0]]), np.array([1, 0]), True)]
+)
+def test_check_target_type_ova(target, output_target, is_ova):
+    converted_target, binarize_target = check_target_type(
+        target.astype(int), indicate_one_vs_all=True)
+    assert_array_equal(converted_target, output_target.astype(int))
+    assert binarize_target == is_ova
+
+
+def test_check_target_warning():
+    target = np.arange(4).reshape((2, 2))
+    with pytest.warns(UserWarning, message='should be of types'):
+        check_target_type(target)
+
 def test_check_ratio_error():
     with raises(ValueError, match="'sampling_type' should be one of"):
         check_ratio('auto', np.array([1, 2, 3]), 'rnd')
diff --git a/imblearn/utils/validation.py b/imblearn/utils/validation.py
index 17fa7e93a..58488463a 100644
--- a/imblearn/utils/validation.py
+++ b/imblearn/utils/validation.py
@@ -10,7 +10,6 @@
 
 import numpy as np
 
-from sklearn.preprocessing import label_binarize
 from sklearn.neighbors.base import KNeighborsMixin
 from sklearn.neighbors import NearestNeighbors
 from sklearn.externals import six, joblib

From 05ae2e6ff833f033086b31b4548545f0f31c4157 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Thu, 1 Mar 2018 02:30:45 +0100
Subject: [PATCH 3/9] PEP8

---
 imblearn/utils/tests/test_validation.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/imblearn/utils/tests/test_validation.py b/imblearn/utils/tests/test_validation.py
index 49e9d6997..64a23eb4a 100644
--- a/imblearn/utils/tests/test_validation.py
+++ b/imblearn/utils/tests/test_validation.py
@@ -67,6 +67,7 @@ def test_check_target_warning():
     with pytest.warns(UserWarning, message='should be of types'):
         check_target_type(target)
 
+
 def test_check_ratio_error():
     with raises(ValueError, match="'sampling_type' should be one of"):
         check_ratio('auto', np.array([1, 2, 3]), 'rnd')

From 05b7d65798e1baad01316f3e1b9f1913f7bf7cad Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Thu, 1 Mar 2018 02:33:55 +0100
Subject: [PATCH 4/9] TST fix pytests match warns

---
 imblearn/utils/tests/test_validation.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/imblearn/utils/tests/test_validation.py b/imblearn/utils/tests/test_validation.py
index 64a23eb4a..bed62617d 100644
--- a/imblearn/utils/tests/test_validation.py
+++ b/imblearn/utils/tests/test_validation.py
@@ -64,7 +64,7 @@ def test_check_target_type_ova(target, output_target, is_ova):
 
 def test_check_target_warning():
     target = np.arange(4).reshape((2, 2))
-    with pytest.warns(UserWarning, message='should be of types'):
+    with pytest.warns(UserWarning, match='should be of types'):
         check_target_type(target)
 
 

From 1a27e3e80b6020fe63dfc922821a86adac36a5c7 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Thu, 1 Mar 2018 03:26:24 +0100
Subject: [PATCH 5/9] TST common test to check multiclass ova equality

---
 imblearn/base.py                     | 29 +++++++++++++++-------------
 imblearn/combine/smote_enn.py        |  2 +-
 imblearn/combine/smote_tomek.py      |  3 +--
 imblearn/ensemble/balance_cascade.py |  3 ++-
 imblearn/utils/estimator_checks.py   | 18 +++++++++++++++++
 5 files changed, 38 insertions(+), 17 deletions(-)

diff --git a/imblearn/base.py b/imblearn/base.py
index d352d9696..a44831c0b 100644
--- a/imblearn/base.py
+++ b/imblearn/base.py
@@ -15,7 +15,6 @@
 from sklearn.externals import six
 from sklearn.preprocessing import label_binarize
 from sklearn.utils import check_X_y
-from sklearn.utils.multiclass import type_of_target
 from sklearn.utils.validation import check_is_fitted
 
 from .utils import check_ratio, check_target_type, hash_X_y
@@ -245,17 +244,10 @@ def __init__(self, func=None, accept_sparse=True, kw_args=None):
         self.kw_args = kw_args
         self.logger = logging.getLogger(__name__)
 
-    def _check_X_y(self, X, y):
-        if self.accept_sparse:
-            X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
-        else:
-            X, y = check_X_y(X, y, accept_sparse=False)
-        y = check_target_type(y)
-
-        return X, y
-
     def fit(self, X, y):
-        X, y = self._check_X_y(X, y)
+        y = check_target_type(y)
+        X, y = check_X_y(X, y, accept_sparse=['csr', 'csc']
+                         if self.accept_sparse else False)
         self.X_hash_, self.y_hash_ = hash_X_y(X, y)
         # when using a sampler, ratio_ is supposed to exist after fit
         self.ratio_ = 'is_fitted'
@@ -263,7 +255,9 @@ def fit(self, X, y):
         return self
 
     def _sample(self, X, y, func=None, kw_args=None):
-        X, y = self._check_X_y(X, y)
+        y, binarize_y = check_target_type(y, indicate_one_vs_all=True)
+        X, y = check_X_y(X, y, accept_sparse=['csr', 'csc']
+                         if self.accept_sparse else False)
         check_is_fitted(self, 'ratio_')
         X_hash, y_hash = hash_X_y(X, y)
         if self.X_hash_ != X_hash or self.y_hash_ != y_hash:
@@ -272,7 +266,16 @@ def _sample(self, X, y, func=None, kw_args=None):
         if func is None:
             func = _identity
 
-        return func(X, y, **(kw_args if self.kw_args else {}))
+        output = func(X, y, **(kw_args if self.kw_args else {}))
+
+        if binarize_y:
+            y_sampled = label_binarize(output[1], np.unique(y))
+            if len(output) == 2:
+                return output[0], y_sampled
+            else:
+                return output[0], y_sampled, output[2]
+        else:
+            return output
 
     def sample(self, X, y):
         return self._sample(X, y, func=self.func, kw_args=self.kw_args)
diff --git a/imblearn/combine/smote_enn.py b/imblearn/combine/smote_enn.py
index 74420472b..470919878 100644
--- a/imblearn/combine/smote_enn.py
+++ b/imblearn/combine/smote_enn.py
@@ -144,8 +144,8 @@ def fit(self, X, y):
             Return self.
 
         """
-        X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
         y = check_target_type(y)
+        X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
         self.ratio_ = self.ratio
         self.X_hash_, self.y_hash_ = hash_X_y(X, y)
 
diff --git a/imblearn/combine/smote_tomek.py b/imblearn/combine/smote_tomek.py
index b48e6510a..0748e6ef7 100644
--- a/imblearn/combine/smote_tomek.py
+++ b/imblearn/combine/smote_tomek.py
@@ -8,7 +8,6 @@
 from __future__ import division
 
 import logging
-import warnings
 
 from sklearn.utils import check_X_y
 
@@ -153,8 +152,8 @@ def fit(self, X, y):
             Return self.
 
         """
-        X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
         y = check_target_type(y)
+        X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
         self.ratio_ = self.ratio
         self.X_hash_, self.y_hash_ = hash_X_y(X, y)
 
diff --git a/imblearn/ensemble/balance_cascade.py b/imblearn/ensemble/balance_cascade.py
index bc6a06c6f..6668209ea 100644
--- a/imblearn/ensemble/balance_cascade.py
+++ b/imblearn/ensemble/balance_cascade.py
@@ -14,7 +14,7 @@
 from sklearn.model_selection import cross_val_predict
 
 from .base import BaseEnsembleSampler
-from ..utils import check_ratio
+from ..utils import check_ratio, check_target_type
 
 
 class BalanceCascade(BaseEnsembleSampler):
@@ -137,6 +137,7 @@ def fit(self, X, y):
 
         """
         super(BalanceCascade, self).fit(X, y)
+        y = check_target_type(y)
         self.ratio_ = check_ratio(self.ratio, y, 'under-sampling')
         return self
 
diff --git a/imblearn/utils/estimator_checks.py b/imblearn/utils/estimator_checks.py
index 2184b4b12..bd37e408a 100644
--- a/imblearn/utils/estimator_checks.py
+++ b/imblearn/utils/estimator_checks.py
@@ -19,11 +19,13 @@
 
 from sklearn.datasets import make_classification
 from sklearn.cluster import KMeans
+from sklearn.preprocessing import label_binarize
 from sklearn.utils.estimator_checks import check_estimator \
     as sklearn_check_estimator, check_parameters_default_constructible
 from sklearn.exceptions import NotFittedError
 from sklearn.utils.testing import assert_allclose
 from sklearn.utils.testing import set_random_state
+from sklearn.utils.multiclass import type_of_target
 
 from imblearn.over_sampling.base import BaseOverSampler
 from imblearn.under_sampling.base import BaseCleaningSampler, BaseUnderSampler
@@ -44,6 +46,7 @@ def _yield_sampler_checks(name, Estimator):
     yield check_samplers_ratio_fit_sample
     yield check_samplers_sparse
     yield check_samplers_pandas
+    yield check_samplers_multiclass_ova
 
 
 def _yield_all_checks(name, estimator):
@@ -253,3 +256,18 @@ def check_samplers_pandas(name, Sampler):
         X_res, y_res = sampler.fit_sample(X, y)
         assert_allclose(X_res_pd, X_res)
         assert_allclose(y_res_pd, y_res)
+
+
+def check_samplers_multiclass_ova(name, Sampler):
+    # Check that multiclass target lead to the same results than OVA encoding
+    X, y = make_classification(n_samples=1000, n_classes=3,
+                               n_informative=4, weights=[0.2, 0.3, 0.5],
+                               random_state=0)
+    y_ova = label_binarize(y, np.unique(y))
+    sampler = Sampler()
+    set_random_state(sampler)
+    X_res, y_res = sampler.fit_sample(X, y)
+    X_res_ova, y_res_ova = sampler.fit_sample(X, y_ova)
+    assert_allclose(X_res, X_res_ova)
+    assert type_of_target(y_res_ova) == type_of_target(y_ova)
+    assert_allclose(y_res, y_res_ova.argmax(axis=1))

From f5a583f7fdcbc65445ccf232a9f00d673e48250d Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Thu, 1 Mar 2018 15:37:25 +0100
Subject: [PATCH 6/9] FIX/TST ensemble handle one-vs-all encoding

---
 imblearn/ensemble/base.py          | 50 ++++++++++++++++++++++++++++++
 imblearn/utils/estimator_checks.py |  9 ++++--
 2 files changed, 57 insertions(+), 2 deletions(-)

diff --git a/imblearn/ensemble/base.py b/imblearn/ensemble/base.py
index 87fbd8250..bb1d34ebc 100644
--- a/imblearn/ensemble/base.py
+++ b/imblearn/ensemble/base.py
@@ -4,7 +4,14 @@
 # Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
 # License: MIT
 
+import numpy as np
+
+from sklearn.preprocessing import label_binarize
+from sklearn.utils import check_X_y
+from sklearn.utils.validation import check_is_fitted
+
 from ..base import BaseSampler
+from ..utils import check_target_type
 
 
 class BaseEnsembleSampler(BaseSampler):
@@ -15,3 +22,46 @@ class BaseEnsembleSampler(BaseSampler):
     """
 
     _sampling_type = 'ensemble'
+
+    def sample(self, X, y):
+        """Resample the dataset.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix}, shape (n_samples, n_features)
+            Matrix containing the data which have to be sampled.
+
+        y : array-like, shape (n_samples,)
+            Corresponding label for each sample in X.
+
+        Returns
+        -------
+        X_resampled : {ndarray, sparse matrix}, shape \
+(n_subset, n_samples_new, n_features)
+            The array containing the resampled data.
+
+        y_resampled : ndarray, shape (n_subset, n_samples_new)
+            The corresponding label of `X_resampled`
+
+        """
+        # Ensemble are a bit specific since they are returning an array of
+        # resampled arrays.
+        y, binarize_y = check_target_type(y, indicate_one_vs_all=True)
+        X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
+
+        check_is_fitted(self, 'ratio_')
+        self._check_X_y(X, y)
+
+        output = self._sample(X, y)
+
+        if binarize_y:
+            y_resampled = output[1]
+            classes = np.unique(y)
+            y_resampled_encoded = np.array([label_binarize(batch_y, classes)
+                                            for batch_y in y_resampled])
+            if len(output) == 2:
+                return output[0], y_resampled_encoded
+            else:
+                return output[0], y_resampled_encoded, output[2]
+        else:
+            return output
diff --git a/imblearn/utils/estimator_checks.py b/imblearn/utils/estimator_checks.py
index bd37e408a..db4cc2bd9 100644
--- a/imblearn/utils/estimator_checks.py
+++ b/imblearn/utils/estimator_checks.py
@@ -269,5 +269,10 @@ def check_samplers_multiclass_ova(name, Sampler):
     X_res, y_res = sampler.fit_sample(X, y)
     X_res_ova, y_res_ova = sampler.fit_sample(X, y_ova)
     assert_allclose(X_res, X_res_ova)
-    assert type_of_target(y_res_ova) == type_of_target(y_ova)
-    assert_allclose(y_res, y_res_ova.argmax(axis=1))
+    if issubclass(Sampler, BaseEnsembleSampler):
+        for batch_y, batch_y_ova in zip(y_res, y_res_ova):
+            assert type_of_target(batch_y_ova) == type_of_target(y_ova)
+            assert_allclose(batch_y, batch_y_ova.argmax(axis=1))
+    else:
+        assert type_of_target(y_res_ova) == type_of_target(y_ova)
+        assert_allclose(y_res, y_res_ova.argmax(axis=1))

From 1435b3515e81376de7822b29e449405a98dadc32 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 19 Mar 2018 18:46:55 +0100
Subject: [PATCH 7/9] DOC remove wrong documentation module

---
 imblearn/__init__.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/imblearn/__init__.py b/imblearn/__init__.py
index 7803ca016..9f05adb1f 100644
--- a/imblearn/__init__.py
+++ b/imblearn/__init__.py
@@ -13,9 +13,6 @@
 exceptions
     Module including custom warnings and error clases used across
     imbalanced-learn.
-keras
-    Module which provides custom generator, layers for deep learning using
-    keras.
 metrics
     Module which provides metrics to quantified the classification performance
     with imbalanced dataset.

From 6350718f32a0c5f07507276c51cdf3c17df1bddc Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 19 Mar 2018 18:55:21 +0100
Subject: [PATCH 8/9] add whats new entry

---
 doc/whats_new/v0.0.4.rst | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/doc/whats_new/v0.0.4.rst b/doc/whats_new/v0.0.4.rst
index 67b012ae1..66c360e45 100644
--- a/doc/whats_new/v0.0.4.rst
+++ b/doc/whats_new/v0.0.4.rst
@@ -15,6 +15,9 @@ Enhancement
 - Document the metrics to evaluate models on imbalanced dataset. :issue:`367`
   by :user:`Guillaume Lemaitre <glemaitre>`.
 
+- Add support for one-vs-all encoded target to support keras. :issue:`410` by
+  :user:`Guillaume Lemaitre <glemaitre>`.
+
 Bug fixes
 .........
 

From dae94b90f801bfa9be6ae6867c644cbcfc472c41 Mon Sep 17 00:00:00 2001
From: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Date: Mon, 19 Mar 2018 18:55:54 +0100
Subject: [PATCH 9/9] correct issue

---
 doc/whats_new/v0.0.4.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/whats_new/v0.0.4.rst b/doc/whats_new/v0.0.4.rst
index 66c360e45..0e3211b31 100644
--- a/doc/whats_new/v0.0.4.rst
+++ b/doc/whats_new/v0.0.4.rst
@@ -15,7 +15,7 @@ Enhancement
 - Document the metrics to evaluate models on imbalanced dataset. :issue:`367`
   by :user:`Guillaume Lemaitre <glemaitre>`.
 
-- Add support for one-vs-all encoded target to support keras. :issue:`410` by
+- Add support for one-vs-all encoded target to support keras. :issue:`409` by
   :user:`Guillaume Lemaitre <glemaitre>`.
 
 Bug fixes