Update the RENN with test and doc

doc/api.rst

@@ -22,6 +22,7 @@ Classes
  unbalanced_dataset.under_sampling.ClusterCentroids
  unbalanced_dataset.under_sampling.CondensedNearestNeighbour
  unbalanced_dataset.under_sampling.EditedNearestNeighbours
+ unbalanced_dataset.under_sampling.RepeatedEditedNearestNeighbours
  unbalanced_dataset.under_sampling.InstanceHardnessThreshold
  unbalanced_dataset.under_sampling.NearMiss
  unbalanced_dataset.under_sampling.NeighbourhoodCleaningRule

unbalanced_dataset/under_sampling/edited_nearest_neighbours.py

@@ -254,7 +254,7 @@ def transform(self, X, y):
 
 
 class RepeatedEditedNearestNeighbours(UnderSampler):
- """Class to perform under-sampling based on the repeated edited nearest 
+ """Class to perform under-sampling based on the repeated edited nearest
  neighbour method.
 
  Parameters
@@ -317,8 +317,8 @@ class RepeatedEditedNearestNeighbours(UnderSampler):
 
  References
  ----------
- .. [1] I. Tomek, “An Experiment with the Edited Nearest-Neighbor
- Rule,” IEEE Trans. Systems, Man, and Cybernetics, vol. 6, no. 6,
+ .. [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.
 
  """

unbalanced_dataset/under_sampling/tests/test_edited_nearest_neighbours.py

@@ -1,4 +1,4 @@
-"""Test the module condensed nearest neighbour."""
+"""Test the module edited nearest neighbour."""
 from __future__ import print_function
 
 import os
@@ -110,8 +110,6 @@ def test_enn_fit_transform_mode():
  X_resampled, y_resampled = enn.fit_transform(X, Y)
 
  currdir = os.path.dirname(os.path.abspath(__file__))
- np.save(os.path.join(currdir, 'data', 'enn_x_mode.npy'), X_resampled)
- np.save(os.path.join(currdir, 'data', 'enn_y_mode.npy'), y_resampled)
  X_gt = np.load(os.path.join(currdir, 'data', 'enn_x_mode.npy'))
  y_gt = np.load(os.path.join(currdir, 'data', 'enn_y_mode.npy'))
  assert_array_equal(X_resampled, X_gt)

unbalanced_dataset/under_sampling/tests/test_repeated_edited_nearest_neighbours.py

@@ -0,0 +1,121 @@
+"""Test the module repeated edited nearest neighbour."""
+from __future__ import print_function
+
+import os
+
+import numpy as np
+from numpy.testing import assert_raises
+from numpy.testing import assert_equal
+from numpy.testing import assert_array_equal
+from numpy.testing import assert_warns
+
+from sklearn.datasets import make_classification
+from sklearn.utils.estimator_checks import check_estimator
+
+from unbalanced_dataset.under_sampling import RepeatedEditedNearestNeighbours
+
+# Generate a global dataset to use
+RND_SEED = 0
+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=5000, random_state=RND_SEED)
+
+
+def test_renn_init():
+ """Test the initialisation of the object"""
+
+ # Define a ratio
+ verbose = True
+ renn = RepeatedEditedNearestNeighbours(random_state=RND_SEED,
+ verbose=verbose)
+
+ assert_equal(renn.size_ngh, 3)
+ assert_equal(renn.kind_sel, 'all')
+ assert_equal(renn.n_jobs, -1)
+ assert_equal(renn.rs_, RND_SEED)
+ assert_equal(renn.verbose, verbose)
+ assert_equal(renn.min_c_, None)
+ assert_equal(renn.maj_c_, None)
+ assert_equal(renn.stats_c_, {})
+
+
+def test_renn_fit_single_class():
+ """Test either if an error when there is a single class"""
+
+ # Create the object
+ renn = RepeatedEditedNearestNeighbours(random_state=RND_SEED)
+ # Resample the data
+ # Create a wrong y
+ y_single_class = np.zeros((X.shape[0], ))
+ assert_raises(RuntimeError, renn.fit, X, y_single_class)
+
+
+def test_renn_fit():
+ """Test the fitting method"""
+
+ # Create the object
+ renn = RepeatedEditedNearestNeighbours(random_state=RND_SEED)
+ # Fit the data
+ renn.fit(X, Y)
+
+ # Check if the data information have been computed
+ assert_equal(renn.min_c_, 0)
+ assert_equal(renn.maj_c_, 1)
+ assert_equal(renn.stats_c_[0], 500)
+ assert_equal(renn.stats_c_[1], 4500)
+
+
+def test_renn_transform_wt_fit():
+ """Test either if an error is raised when transform is called before
+ fitting"""
+
+ # Create the object
+ renn = RepeatedEditedNearestNeighbours(random_state=RND_SEED)
+ assert_raises(RuntimeError, renn.transform, X, Y)
+
+
+def test_renn_fit_transform():
+ """Test the fit transform routine"""
+
+ # Resample the data
+ renn = RepeatedEditedNearestNeighbours(random_state=RND_SEED)
+ X_resampled, y_resampled = renn.fit_transform(X, Y)
+
+ currdir = os.path.dirname(os.path.abspath(__file__))
+ X_gt = np.load(os.path.join(currdir, 'data', 'renn_x.npy'))
+ y_gt = np.load(os.path.join(currdir, 'data', 'renn_y.npy'))
+ assert_array_equal(X_resampled, X_gt)
+ assert_array_equal(y_resampled, y_gt)
+
+
+def test_renn_fit_transform_with_indices():
+ """Test the fit transform routine with indices support"""
+
+ # Resample the data
+ renn = RepeatedEditedNearestNeighbours(return_indices=True,
+ random_state=RND_SEED)
+ X_resampled, y_resampled, idx_under = renn.fit_transform(X, Y)
+
+ currdir = os.path.dirname(os.path.abspath(__file__))
+ X_gt = np.load(os.path.join(currdir, 'data', 'renn_x.npy'))
+ y_gt = np.load(os.path.join(currdir, 'data', 'renn_y.npy'))
+ idx_gt = np.load(os.path.join(currdir, 'data', 'renn_idx.npy'))
+ assert_array_equal(X_resampled, X_gt)
+ assert_array_equal(y_resampled, y_gt)
+ assert_array_equal(idx_under, idx_gt)
+
+
+def test_renn_fit_transform_mode():
+ """Test the fit transform routine using the mode as selection"""
+
+ # Resample the data
+ renn = RepeatedEditedNearestNeighbours(random_state=RND_SEED,
+ kind_sel='mode')
+ X_resampled, y_resampled = renn.fit_transform(X, Y)
+
+ currdir = os.path.dirname(os.path.abspath(__file__))
+ X_gt = np.load(os.path.join(currdir, 'data', 'renn_x_mode.npy'))
+ y_gt = np.load(os.path.join(currdir, 'data', 'renn_y_mode.npy'))
+ assert_array_equal(X_resampled, X_gt)
+ assert_array_equal(y_resampled, y_gt)