MAINT Housekeeping remove deprecated parameters (#332)

* MAINT Housekeeping remove deprecated parameters

* TST solve error clustercentroids

* TST remove useless tests

Author: glemaitre

doc/whats_new.rst

@@ -4,8 +4,10 @@
 Release history
 ===============
 
+.. include:: whats_new/v0.0.4.rst
+
 .. include:: whats_new/v0.0.3.rst
-             
+
 .. include:: whats_new/v0.0.2.rst
 
 .. include:: whats_new/v0.0.1.rst

doc/whats_new/v0.0.4.rst

@@ -0,0 +1,13 @@
+.. _changes_0_3:
+
+Version 0.4 (under development)
+===============================
+
+Changelog
+---------
+
+Maintenance
+...........
+
+- Remove deprecated parameters in 0.2 - :issue:`331` by :user:`Guillaume
+  Lemaitre <glemaitre>`.

imblearn/combine/smote_enn.py

@@ -7,7 +7,6 @@
 from __future__ import division
 
 import logging
-import warnings
 
 from sklearn.utils import check_X_y
 
@@ -54,82 +53,6 @@ class SMOTEENN(SamplerMixin):
         a :class:`imblearn.over_sampling.SMOTE` object with default parameters
         will be given.
 
-    enn : object, optional (default=EditedNearestNeighbours())
-        The :class:`imblearn.under_sampling.EditedNearestNeighbours` object to
-        use. If not given, an
-        :class:`imblearn.under_sampling.EditedNearestNeighbours` object with
-        default parameters will be given.
-
-    k : int, optional (default=None)
-        Number of nearest neighbours to used to construct synthetic
-        samples.
-
-        .. deprecated:: 0.2
-           `k` is deprecated from 0.2 and will be replaced in 0.4
-           Give directly a :class:`imblearn.over_sampling.SMOTE` object.
-
-    m : int, optional (default=None)
-        Number of nearest neighbours to use to determine if a minority
-        sample is in danger.
-
-        .. deprecated:: 0.2
-           `m` is deprecated from 0.2 and will be replaced in 0.4
-           Give directly a :class:`imblearn.over_sampling.SMOTE` object.
-
-    out_step : float, optional (default=None)
-        Step size when extrapolating.
-
-        .. deprecated:: 0.2
-           ``out_step`` is deprecated from 0.2 and will be replaced in 0.4
-           Give directly a :class:`imblearn.over_sampling.SMOTE` object.
-
-    kind_smote : str, optional (default=None)
-        The type of SMOTE algorithm to use one of the following
-        options: ``'regular'``, ``'borderline1'``, ``'borderline2'``,
-        ``'svm'``.
-
-        .. deprecated:: 0.2
-           `kind_smote` is deprecated from 0.2 and will be replaced in 0.4
-           Give directly a :class:`imblearn.over_sampling.SMOTE` object.
-
-    size_ngh : int, optional (default=None)
-        Size of the neighbourhood to consider to compute the average
-        distance to the minority point samples.
-
-        .. deprecated:: 0.2
-           size_ngh is deprecated from 0.2 and will be replaced in 0.4
-           Use ``n_neighbors`` instead.
-
-    n_neighbors : int, optional (default=None)
-        Size of the neighbourhood to consider to compute the average
-        distance to the minority point samples.
-
-        .. deprecated:: 0.2
-           `n_neighbors` is deprecated from 0.2 and will be replaced in 0.4
-           Give directly a
-           :class:`imblearn.under_sampling.EditedNearestNeighbours` object.
-
-    kind_sel : str, optional (default=None)
-        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.
-
-        .. deprecated:: 0.2
-           ``kind_sel`` is deprecated from 0.2 and will be replaced in 0.4 Give
-           directly a :class:`imblearn.under_sampling.EditedNearestNeighbours`
-           object.
-
-    n_jobs : int, optional (default=None)
-        The number of threads to open if possible.
-
-        .. deprecated:: 0.2
-           `n_jobs` is deprecated from 0.2 and will be replaced in 0.4 Give
-           directly a :class:`imblearn.over_sampling.SMOTE` and
-           :class:`imblearn.under_sampling.EditedNearestNeighbours` object.
-
     Notes
     -----
     The method is presented in [1]_.
@@ -173,65 +96,17 @@ def __init__(self,
                  ratio='auto',
                  random_state=None,
                  smote=None,
-                 enn=None,
-                 k=None,
-                 m=None,
-                 out_step=None,
-                 kind_smote=None,
-                 size_ngh=None,
-                 n_neighbors=None,
-                 kind_enn=None,
-                 n_jobs=None):
+                 enn=None):
         super(SMOTEENN, self).__init__()
         self.ratio = ratio
         self.random_state = random_state
         self.smote = smote
         self.enn = enn
-        self.k = k
-        self.m = m
-        self.out_step = out_step
-        self.kind_smote = kind_smote
-        self.size_ngh = size_ngh
-        self.n_neighbors = n_neighbors
-        self.kind_enn = kind_enn
-        self.n_jobs = n_jobs
         self.logger = logging.getLogger(__name__)
 
     def _validate_estimator(self):
         "Private function to validate SMOTE and ENN objects"
-
-        # Check any parameters for SMOTE was provided
-        # Anounce deprecation
-        if (self.k is not None or self.m is not None or
-                self.out_step is not None or self.kind_smote is not None or
-                self.n_jobs is not None):
-            # We need to list each parameter and decide if we affect a default
-            # value or not
-            if self.k is None:
-                self.k = 5
-            if self.m is None:
-                self.m = 10
-            if self.out_step is None:
-                self.out_step = 0.5
-            if self.kind_smote is None:
-                self.kind_smote = 'regular'
-            if self.n_jobs is None:
-                smote_jobs = 1
-            else:
-                smote_jobs = self.n_jobs
-            warnings.warn('Parameters initialization will be replaced in'
-                          ' version 0.4. Use a SMOTE object instead.',
-                          DeprecationWarning)
-            self.smote_ = SMOTE(
-                ratio=self.ratio,
-                random_state=self.random_state,
-                k=self.k,
-                m=self.m,
-                out_step=self.out_step,
-                kind=self.kind_smote,
-                n_jobs=smote_jobs)
-        # If an object was given, affect
-        elif self.smote is not None:
+        if self.smote is not None:
             if isinstance(self.smote, SMOTE):
                 self.smote_ = self.smote
             else:
@@ -242,30 +117,7 @@ def _validate_estimator(self):
             self.smote_ = SMOTE(
                 ratio=self.ratio, random_state=self.random_state)
 
-        # Check any parameters for ENN was provided
-        # Anounce deprecation
-        if (self.size_ngh is not None or self.n_neighbors is not None or
-                self.kind_enn is not None or self.n_jobs is not None):
-            warnings.warn('Parameters initialization will be replaced in'
-                          ' version 0.4. Use a ENN object instead.',
-                          DeprecationWarning)
-            # We need to list each parameter and decide if we affect a default
-            # value or not
-            if self.n_neighbors is None:
-                self.n_neighbors = 3
-            if self.kind_enn is None:
-                self.kind_enn = 'all'
-            if self.n_jobs is None:
-                self.n_jobs = 1
-            self.enn_ = EditedNearestNeighbours(
-                ratio='all',
-                random_state=self.random_state,
-                size_ngh=self.size_ngh,
-                n_neighbors=self.n_neighbors,
-                kind_sel=self.kind_enn,
-                n_jobs=self.n_jobs)
-        # If an object was given, affect
-        elif self.enn is not None:
+        if self.enn is not None:
             if isinstance(self.enn, EditedNearestNeighbours):
                 self.enn_ = self.enn
             else:

imblearn/combine/smote_tomek.py

@@ -61,45 +61,6 @@ class SMOTETomek(SamplerMixin):
         a :class:`imblearn.under_sampling.Tomek` object with default parameters
         will be given.
 
-    k : int, optional (default=None)
-        Number of nearest neighbours to used to construct synthetic
-        samples.
-
-        .. deprecated:: 0.2
-           ``k`` is deprecated from 0.2 and will be replaced in 0.4
-           Give directly a :class:`imblearn.over_sampling.SMOTE` object.
-
-    m : int, optional (default=None)
-        Number of nearest neighbours to use to determine if a minority
-        sample is in danger.
-
-        .. deprecated:: 0.2
-           ``m`` is deprecated from 0.2 and will be replaced in 0.4
-           Give directly a :class:`imblearn.over_sampling.SMOTE` object.
-
-    out_step : float, optional (default=None)
-        Step size when extrapolating.
-
-        .. deprecated:: 0.2
-           ``out_step`` is deprecated from 0.2 and will be replaced in 0.4
-           Give directly a :class:`imblearn.over_sampling.SMOTE` object.
-
-    kind_smote : str, optional (default=None)
-        The type of SMOTE algorithm to use one of the following
-        options: ``'regular'``, ``'borderline1'``, ``'borderline2'``,
-        ``'svm'``.
-
-        .. deprecated:: 0.2
-           ``kind_smote`` is deprecated from 0.2 and will be replaced in 0.4
-           Give directly a :class:`imblearn.over_sampling.SMOTE` object.
-
-    n_jobs : int, optional (default=None)
-        The number of threads to open if possible.
-
-        .. deprecated:: 0.2
-           ``n_jobs`` is deprecated from 0.2 and will be replaced in 0.4
-           Give directly a :class:`imblearn.over_sampling.SMOTE` object.
-
     Notes
     -----
     The methos is presented in [1]_.
@@ -143,59 +104,18 @@ def __init__(self,
                  ratio='auto',
                  random_state=None,
                  smote=None,
-                 tomek=None,
-                 k=None,
-                 m=None,
-                 out_step=None,
-                 kind_smote=None,
-                 n_jobs=None):
+                 tomek=None):
         super(SMOTETomek, self).__init__()
         self.ratio = ratio
         self.random_state = random_state
         self.smote = smote
         self.tomek = tomek
-        self.k = k
-        self.m = m
-        self.out_step = out_step
-        self.kind_smote = kind_smote
-        self.n_jobs = n_jobs
         self.logger = logging.getLogger(__name__)
 
     def _validate_estimator(self):
         "Private function to validate SMOTE and ENN objects"
 
-        # Check any parameters for SMOTE was provided
-        # Anounce deprecation
-        if (self.k is not None or self.m is not None or
-                self.out_step is not None or self.kind_smote is not None or
-                self.n_jobs is not None):
-            warnings.warn('Parameters initialization will be replaced in'
-                          ' version 0.4. Use a SMOTE object instead.',
-                          DeprecationWarning)
-            # We need to list each parameter and decide if we affect a default
-            # value or not
-            if self.k is None:
-                self.k = 5
-            if self.m is None:
-                self.m = 10
-            if self.out_step is None:
-                self.out_step = 0.5
-            if self.kind_smote is None:
-                self.kind_smote = 'regular'
-            if self.n_jobs is None:
-                smote_jobs = 1
-            else:
-                smote_jobs = self.n_jobs
-            self.smote_ = SMOTE(
-                ratio=self.ratio,
-                random_state=self.random_state,
-                k=self.k,
-                m=self.m,
-                out_step=self.out_step,
-                kind=self.kind_smote,
-                n_jobs=smote_jobs)
-        # If an object was given, affect
-        elif self.smote is not None:
+        if self.smote is not None:
             if isinstance(self.smote, SMOTE):
                 self.smote_ = self.smote
             else:
@@ -206,17 +126,7 @@ def _validate_estimator(self):
             self.smote_ = SMOTE(
                 ratio=self.ratio, random_state=self.random_state)
 
-        # Check any parameters for ENN was provided
-        # Anounce deprecation
-        if self.n_jobs is not None:
-            warnings.warn('Parameters initialization will be replaced in'
-                          ' version 0.4. Use a ENN object instead.',
-                          DeprecationWarning)
-            self.tomek_ = TomekLinks(ratio='all',
-                                     random_state=self.random_state,
-                                     n_jobs=self.n_jobs)
-        # If an object was given, affect
-        elif self.tomek is not None:
+        if self.tomek is not None:
             if isinstance(self.tomek, TomekLinks):
                 self.tomek_ = self.tomek
             else:

imblearn/combine/tests/test_smote_enn.py

@@ -45,8 +45,27 @@ def test_sample_regular():
     assert_array_equal(y_resampled, y_gt)
 
 
+def test_sample_regular_pass_smote_enn():
+    smote = SMOTEENN(smote=SMOTE(ratio='auto', random_state=RND_SEED),
+                     enn=EditedNearestNeighbours(ratio='all',
+                                                 random_state=RND_SEED),
+                     random_state=RND_SEED)
+    X_resampled, y_resampled = smote.fit_sample(X, Y)
+
+    X_gt = np.array([[1.52091956, -0.49283504],
+                     [0.84976473, -0.15570176],
+                     [0.61319159, -0.11571667],
+                     [0.66052536, -0.28246518],
+                     [-0.28162401, -2.10400981],
+                     [0.83680821, 1.72827342],
+                     [0.08711622, 0.93259929]])
+    y_gt = np.array([0, 0, 0, 0, 1, 1, 1])
+    assert_allclose(X_resampled, X_gt, rtol=R_TOL)
+    assert_array_equal(y_resampled, y_gt)
+
+
 def test_sample_regular_half():
-    ratio = 0.8
+    ratio = {0: 10, 1: 12}
     smote = SMOTEENN(ratio=ratio, random_state=RND_SEED)
     X_resampled, y_resampled = smote.fit_sample(X, Y)
 
@@ -91,25 +110,6 @@ def test_validate_estimator_default():
     assert_array_equal(y_resampled, y_gt)
 
 
-def test_validate_estimator_deprecation():
-    smt = SMOTEENN(random_state=RND_SEED)
-    X_resampled, y_resampled = smt.fit_sample(X, Y)
-    X_gt = np.array([[1.52091956, -0.49283504],
-                     [0.84976473, -0.15570176],
-                     [0.61319159, -0.11571667],
-                     [0.66052536, -0.28246518],
-                     [-0.28162401, -2.10400981],
-                     [0.83680821, 1.72827342],
-                     [0.08711622, 0.93259929]])
-    y_gt = np.array([0, 0, 0, 0, 1, 1, 1])
-    assert_allclose(X_resampled, X_gt, rtol=R_TOL)
-    assert_array_equal(y_resampled, y_gt)
-    smt = SMOTEENN(random_state=RND_SEED, k=5)
-    X_resampled, y_resampled = smt.fit_sample(X, Y)
-    assert_allclose(X_resampled, X_gt, rtol=R_TOL)
-    assert_array_equal(y_resampled, y_gt)
-
-
 def test_error_wrong_object():
     smote = 'rnd'
     enn = 'rnd'