Fixes regarding sklearn upgrade

multi_imbalance/datasets/_data_loader.py

@@ -24,7 +24,7 @@
 
 import numpy as np
 
-from sklearn.datasets.base import Bunch
+from sklearn.datasets._base import Bunch
 
 PRE_FILENAME = 'x'
 POST_FILENAME = 'data.npz'

multi_imbalance/ensemble/tests/test_mrbbagging.py

@@ -1,7 +1,7 @@
 import unittest
 from unittest.mock import MagicMock
 
-from sklearn.tree import tree
+from sklearn.tree import DecisionTreeClassifier
 
 from multi_imbalance.ensemble.mrbbagging import MRBBagging
 import numpy as np
@@ -36,47 +36,47 @@
 
 class TestMRBBagging(unittest.TestCase):
  def test_api(self):
- mrbbagging = MRBBagging(1, tree.DecisionTreeClassifier(random_state=0), random_state=0)
+ mrbbagging = MRBBagging(1, DecisionTreeClassifier(random_state=0), random_state=0)
  mrbbagging.fit(X_train, y_train)
  y_pred = mrbbagging.predict(X_test)
  assert all(y_pred == y_test)
 
  def test_api_multiple_trees(self):
- mrbbagging = MRBBagging(5, tree.DecisionTreeClassifier(random_state=0), random_state=0)
+ mrbbagging = MRBBagging(5, DecisionTreeClassifier(random_state=0), random_state=0)
  mrbbagging.fit(X_train, y_train)
  y_pred = mrbbagging.predict(X_test)
  assert all(y_pred == y_test)
 
  def test_api_with_feature_selection(self):
- mrbbagging = MRBBagging(1, tree.DecisionTreeClassifier(random_state=0), feature_selection=True, random_state=0)
+ mrbbagging = MRBBagging(1, DecisionTreeClassifier(random_state=0), feature_selection=True, random_state=0)
  mrbbagging.fit(X_train, y_train)
  y_pred = mrbbagging.predict(X_test)
  assert all(y_pred == y_test)
 
  def test_api_with_random_feature_selection(self):
- mrbbagging = MRBBagging(1, tree.DecisionTreeClassifier(random_state=0), feature_selection=True, random_fs=True,
+ mrbbagging = MRBBagging(1, DecisionTreeClassifier(random_state=0), feature_selection=True, random_fs=True,
  random_state=0)
  mrbbagging.fit(X_train, y_train)
  y_pred = mrbbagging.predict(X_test)
  assert all(y_pred == y_test)
 
  def test_api_with_feature_selection_sqrt_features(self):
- mrbbagging = MRBBagging(1, tree.DecisionTreeClassifier(random_state=0), feature_selection=True,
+ mrbbagging = MRBBagging(1, DecisionTreeClassifier(random_state=0), feature_selection=True,
  half_features=False, random_state=0)
  mrbbagging.fit(X_train, y_train)
  y_pred = mrbbagging.predict(X_test)
  assert all(y_pred == y_test)
 
  def test__group_data(self):
- mrbbagging = MRBBagging(1, tree.DecisionTreeClassifier())
+ mrbbagging = MRBBagging(1, DecisionTreeClassifier())
  x = [[1, 1, 1], [2, 2, 2], [3, 3, 3]]
  y = ["A", "B", "C"]
  classes, grouped_data = mrbbagging._group_data(x, y)
  self.assertEqual(classes, {'A', 'B', 'C'})
  self.assertEqual(grouped_data, {'C': [[[3, 3, 3], 'C']], 'A': [[[1, 1, 1], 'A']], 'B': [[[2, 2, 2], 'B']]})
 
  def test__group_data_with_none(self):
- mrbbagging = MRBBagging(1, tree.DecisionTreeClassifier())
+ mrbbagging = MRBBagging(1, DecisionTreeClassifier())
  x = [[1, 1, 1], [2, 2, 2], [3, 3, 3]]
  y = ["A", None, "C"]
  with self.assertRaises(AssertionError):
@@ -95,7 +95,7 @@ def test_fit_with_invalid_classifier(self):
 
  def test_with_invalid_k(self):
  with self.assertRaises(AssertionError):
- MRBBagging(0, tree.DecisionTreeClassifier())
+ MRBBagging(0, DecisionTreeClassifier())
 
 
 if __name__ == '__main__':

setup.py

@@ -29,7 +29,7 @@
  "scikit-learn>=0.22.1",
  "pandas>=0.25.1",
  "pytest>=5.1.2",
- "imbalanced-learn==0.6.1",
+ "imbalanced-learn>=0.6.1",
  "coverage>=5.1",
  "pytest-cov>=2.8.1",
  "IPython>=7.13.0",