[MRG] Address issue #113 - Create toy example for testing (#118)

* Create toy and remove npy for RUS

* Create toy and remove npy for ROS

* Change the testing for CC

* Add test for ENN

* Add toy testing for CNN

* Make toy testing for IHT

* Add toy testing for NM

* Add toy testing for OSS

* Add toy testing for TL

* Toy testing for SMOTE

* Toy testing for ADASYN

* Toy testing for SMOTEENN

* Toy testing for SMOTETomek

* Toy testing for BC

* Toy testing for EE

* Toy testing for RENN

* Toy testing for AllKNN

* Toy testing for NCL

* Remove the unecessary files

* Change the kernel of SVM to be linear

* Improve the testing of IHT

* Python 3 compatibility dict and some smells correction

* Fix the ratio type issue

Author: glemaitre

.coveragerc

@@ -17,4 +17,5 @@ exclude_lines =
     raise NotImplementedError
     if 0:
     if __name__ == .__main__.:
-    if self.verbose:
+    if self.verbose:
+show_missing = True

examples/datasets/plot_make_imbalance.py

@@ -9,8 +9,6 @@
 
 print(__doc__)
 
-import numpy as np
-
 import matplotlib.pyplot as plt
 import seaborn as sns
 sns.set()

imblearn/combine/tests/data/smote_enn_reg_x.npy

@@ -7,6 +7,7 @@
 from numpy.testing import assert_raises
 from numpy.testing import assert_equal
 from numpy.testing import assert_array_equal
+from numpy.testing import assert_array_almost_equal
 from numpy.testing import assert_warns
 
 from sklearn.datasets import make_classification
@@ -16,10 +17,27 @@
 
 # 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)
+X = np.array([[0.11622591, -0.0317206],
+              [0.77481731, 0.60935141],
+              [1.25192108, -0.22367336],
+              [0.53366841, -0.30312976],
+              [1.52091956, -0.49283504],
+              [-0.28162401, -2.10400981],
+              [0.83680821, 1.72827342],
+              [0.3084254, 0.33299982],
+              [0.70472253, -0.73309052],
+              [0.28893132, -0.38761769],
+              [1.15514042, 0.0129463],
+              [0.88407872, 0.35454207],
+              [1.31301027, -0.92648734],
+              [-1.11515198, -0.93689695],
+              [-0.18410027, -0.45194484],
+              [0.9281014, 0.53085498],
+              [-0.14374509, 0.27370049],
+              [-0.41635887, -0.38299653],
+              [0.08711622, 0.93259929],
+              [1.70580611, -0.11219234]])
+Y = np.array([0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0])
 
 
 def test_senn_sk_estimator():
@@ -74,8 +92,8 @@ def test_smote_fit():
     # Check if the data information have been computed
     assert_equal(smote.min_c_, 0)
     assert_equal(smote.maj_c_, 1)
-    assert_equal(smote.stats_c_[0], 500)
-    assert_equal(smote.stats_c_[1], 4500)
+    assert_equal(smote.stats_c_[0], 8)
+    assert_equal(smote.stats_c_[1], 12)
 
 
 def test_smote_sample_wt_fit():
@@ -97,28 +115,51 @@ def test_sample_regular():
 
     X_resampled, y_resampled = smote.fit_sample(X, Y)
 
-    currdir = os.path.dirname(os.path.abspath(__file__))
-    X_gt = np.load(os.path.join(currdir, 'data', 'smote_enn_reg_x.npy'))
-    y_gt = np.load(os.path.join(currdir, 'data', 'smote_enn_reg_y.npy'))
-    assert_array_equal(X_resampled, X_gt)
+    X_gt = np.array([[0.11622591, -0.0317206],
+                     [1.25192108, -0.22367336],
+                     [0.53366841, -0.30312976],
+                     [1.52091956, -0.49283504],
+                     [0.88407872, 0.35454207],
+                     [1.31301027, -0.92648734],
+                     [-0.41635887, -0.38299653],
+                     [1.70580611, -0.11219234],
+                     [0.29307743, -0.14670439],
+                     [0.84976473, -0.15570176],
+                     [0.61319159, -0.11571668],
+                     [0.66052536, -0.28246517],
+                     [-0.28162401, -2.10400981],
+                     [0.83680821, 1.72827342],
+                     [0.08711622, 0.93259929]])
+    y_gt = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1])
+    assert_array_almost_equal(X_resampled, X_gt)
     assert_array_equal(y_resampled, y_gt)
 
 
 def test_sample_regular_half():
     """Test sample function with regular SMOTE and a ratio of 0.5."""
 
     # Create the object
-    ratio = 0.5
+    ratio = 0.8
     smote = SMOTEENN(ratio=ratio, random_state=RND_SEED)
     # Fit the data
     smote.fit(X, Y)
 
     X_resampled, y_resampled = smote.fit_sample(X, Y)
 
-    currdir = os.path.dirname(os.path.abspath(__file__))
-    X_gt = np.load(os.path.join(currdir, 'data', 'smote_enn_reg_x_05.npy'))
-    y_gt = np.load(os.path.join(currdir, 'data', 'smote_enn_reg_y_05.npy'))
-    assert_array_equal(X_resampled, X_gt)
+    X_gt = np.array([[0.11622591, -0.0317206],
+                     [1.25192108, -0.22367336],
+                     [0.53366841, -0.30312976],
+                     [1.52091956, -0.49283504],
+                     [0.88407872, 0.35454207],
+                     [1.31301027, -0.92648734],
+                     [-0.41635887, -0.38299653],
+                     [1.70580611, -0.11219234],
+                     [0.36784496, -0.1953161],
+                     [-0.28162401, -2.10400981],
+                     [0.83680821, 1.72827342],
+                     [0.08711622, 0.93259929]])
+    y_gt = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1])
+    assert_array_almost_equal(X_resampled, X_gt)
     assert_array_equal(y_resampled, y_gt)
 
 
@@ -138,11 +179,11 @@ def test_senn_multiclass_error():
     type. """
 
     # continuous case
-    y = np.linspace(0, 1, 5000)
+    y = np.linspace(0, 1, 20)
     sm = SMOTEENN(random_state=RND_SEED)
     assert_warns(UserWarning, sm.fit, X, y)
 
     # multiclass case
-    y = np.array([0] * 2000 + [1] * 2000 + [2] * 1000)
+    y = np.array([0] * 3 + [1] * 2 + [2] * 15)
     sm = SMOTEENN(random_state=RND_SEED)
     assert_warns(UserWarning, sm.fit, X, y)

imblearn/combine/tests/data/smote_enn_reg_x_05.npy

@@ -7,6 +7,7 @@
 from numpy.testing import assert_raises
 from numpy.testing import assert_equal
 from numpy.testing import assert_array_equal
+from numpy.testing import assert_array_almost_equal
 from numpy.testing import assert_warns
 
 from sklearn.datasets import make_classification
@@ -16,10 +17,27 @@
 
 # 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)
+X = np.array([[0.20622591, 0.0582794],
+              [0.68481731, 0.51935141],
+              [1.34192108, -0.13367336],
+              [0.62366841, -0.21312976],
+              [1.61091956, -0.40283504],
+              [-0.37162401, -2.19400981],
+              [0.74680821, 1.63827342],
+              [0.2184254, 0.24299982],
+              [0.61472253, -0.82309052],
+              [0.19893132, -0.47761769],
+              [1.06514042, -0.0770537],
+              [0.97407872, 0.44454207],
+              [1.40301027, -0.83648734],
+              [-1.20515198, -1.02689695],
+              [-0.27410027, -0.54194484],
+              [0.8381014, 0.44085498],
+              [-0.23374509, 0.18370049],
+              [-0.32635887, -0.29299653],
+              [-0.00288378, 0.84259929],
+              [1.79580611, -0.02219234]])
+Y = np.array([0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0])
 
 
 def test_smote_sk_estimator():
@@ -74,8 +92,8 @@ def test_smote_fit():
     # Check if the data information have been computed
     assert_equal(smote.min_c_, 0)
     assert_equal(smote.maj_c_, 1)
-    assert_equal(smote.stats_c_[0], 500)
-    assert_equal(smote.stats_c_[1], 4500)
+    assert_equal(smote.stats_c_[0], 8)
+    assert_equal(smote.stats_c_[1], 12)
 
 
 def test_smote_sample_wt_fit():
@@ -97,28 +115,62 @@ def test_sample_regular():
 
     X_resampled, y_resampled = smote.fit_sample(X, Y)
 
-    currdir = os.path.dirname(os.path.abspath(__file__))
-    X_gt = np.load(os.path.join(currdir, 'data', 'smote_tomek_reg_x.npy'))
-    y_gt = np.load(os.path.join(currdir, 'data', 'smote_tomek_reg_y.npy'))
-    assert_array_equal(X_resampled, X_gt)
+    X_gt = np.array([[0.20622591, 0.0582794],
+                     [0.68481731, 0.51935141],
+                     [1.34192108, -0.13367336],
+                     [0.62366841, -0.21312976],
+                     [1.61091956, -0.40283504],
+                     [-0.37162401, -2.19400981],
+                     [0.74680821, 1.63827342],
+                     [0.61472253, -0.82309052],
+                     [0.19893132, -0.47761769],
+                     [0.97407872, 0.44454207],
+                     [1.40301027, -0.83648734],
+                     [-1.20515198, -1.02689695],
+                     [-0.23374509, 0.18370049],
+                     [-0.32635887, -0.29299653],
+                     [-0.00288378, 0.84259929],
+                     [1.79580611, -0.02219234],
+                     [0.38307743, -0.05670439],
+                     [0.93976473, -0.06570176],
+                     [0.70319159, -0.02571668],
+                     [0.75052536, -0.19246517]])
+    y_gt = np.array([0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0,
+                     0])
+    assert_array_almost_equal(X_resampled, X_gt)
     assert_array_equal(y_resampled, y_gt)
 
 
 def test_sample_regular_half():
     """Test sample function with regular SMOTE and a ratio of 0.5."""
 
     # Create the object
-    ratio = 0.5
+    ratio = 0.8
     smote = SMOTETomek(ratio=ratio, random_state=RND_SEED)
     # Fit the data
     smote.fit(X, Y)
 
     X_resampled, y_resampled = smote.fit_sample(X, Y)
 
-    currdir = os.path.dirname(os.path.abspath(__file__))
-    X_gt = np.load(os.path.join(currdir, 'data', 'smote_tomek_reg_x_05.npy'))
-    y_gt = np.load(os.path.join(currdir, 'data', 'smote_tomek_reg_y_05.npy'))
-    assert_array_equal(X_resampled, X_gt)
+    X_gt = np.array([[0.20622591, 0.0582794],
+                     [0.68481731, 0.51935141],
+                     [1.34192108, -0.13367336],
+                     [0.62366841, -0.21312976],
+                     [1.61091956, -0.40283504],
+                     [-0.37162401, -2.19400981],
+                     [0.74680821, 1.63827342],
+                     [0.61472253, -0.82309052],
+                     [0.19893132, -0.47761769],
+                     [0.97407872, 0.44454207],
+                     [1.40301027, -0.83648734],
+                     [-1.20515198, -1.02689695],
+                     [-0.23374509, 0.18370049],
+                     [-0.32635887, -0.29299653],
+                     [-0.00288378, 0.84259929],
+                     [1.79580611, -0.02219234],
+                     [0.45784496, -0.1053161]])
+    y_gt = np.array([0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0])
+    assert_array_almost_equal(X_resampled, X_gt)
     assert_array_equal(y_resampled, y_gt)
 
 
@@ -138,11 +190,11 @@ def test_multiclass_error():
     type. """
 
     # continuous case
-    y = np.linspace(0, 1, 5000)
+    y = np.linspace(0, 1, 20)
     sm = SMOTETomek(random_state=RND_SEED)
     assert_warns(UserWarning, sm.fit, X, y)
 
     # multiclass case
-    y = np.array([0] * 2000 + [1] * 2000 + [2] * 1000)
+    y = np.array([0] * 3 + [1] * 2 + [2] * 15)
     sm = SMOTETomek(random_state=RND_SEED)
     assert_warns(UserWarning, sm.fit, X, y)

imblearn/combine/tests/data/smote_enn_reg_y.npy

@@ -49,8 +49,13 @@ def make_imbalance(X, y, ratio, min_c_=None, random_state=None):
         The corresponding label of `X_resampled`
 
     """
-    if ratio <= 0.0 or ratio >= 1.0:
-        raise ValueError('ratio value must be such that 0.0 < ratio < 1.0')
+    if isinstance(ratio, float):
+        if ratio > 1:
+            raise ValueError('Ration cannot be greater than one.')
+        elif ratio <= 0:
+            raise ValueError('Ratio cannot be negative.')
+    else:
+        raise ValueError('Ratio must be a float between 0.0 < ratio < 1.0')
 
     X, y = check_X_y(X, y)