compatible with sklearn 00.24 & pytest

multimodal/boosting/combo.py

@@ -127,13 +127,13 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
     >>> views_ind = [0, 2, 4]  # view 0: sepal data, view 1: petal data
     >>> clf = MuComboClassifier(random_state=0)
     >>> clf.fit(X, y, views_ind)  # doctest: +NORMALIZE_WHITESPACE
-    MuComboClassifier(base_estimator=None, n_estimators=50, random_state=0)
+    MuComboClassifier(random_state=0)
     >>> print(clf.predict([[ 5.,  3.,  1.,  1.]]))
     [0]
     >>> views_ind = [[0, 2], [1, 3]]  # view 0: length data, view 1: width data
     >>> clf = MuComboClassifier(random_state=0)
     >>> clf.fit(X, y, views_ind)  # doctest: +NORMALIZE_WHITESPACE
-    MuComboClassifier(base_estimator=None, n_estimators=50, random_state=0)
+    MuComboClassifier(random_state=0)
     >>> print(clf.predict([[ 5.,  3.,  1.,  1.]]))
     [0]
 
@@ -141,13 +141,8 @@ class MuComboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
     >>> base_estimator = DecisionTreeClassifier(max_depth=2)
     >>> clf = MuComboClassifier(base_estimator=base_estimator, random_state=1)
     >>> clf.fit(X, y, views_ind)  # doctest: +NORMALIZE_WHITESPACE
-    MuComboClassifier(base_estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini', max_depth=2,
-            max_features=None, max_leaf_nodes=None,
-            min_impurity_decrease=0.0, min_impurity_split=None,
-            min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, presort='deprecated', random_state=None,
-            splitter='best'),
-         n_estimators=50, random_state=1)
+    MuComboClassifier(base_estimator=DecisionTreeClassifier(max_depth=2),
+                      random_state=1)
     >>> print(clf.predict([[ 5.,  3.,  1.,  1.]]))
     [0]
 

multimodal/boosting/mumbo.py

@@ -127,15 +127,13 @@ class MumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
     >>> views_ind = [0, 2, 4]  # view 0: sepal data, view 1: petal data
     >>> clf = MumboClassifier(random_state=0)
     >>> clf.fit(X, y, views_ind)  # doctest: +NORMALIZE_WHITESPACE
-    MumboClassifier(base_estimator=None, best_view_mode='edge',
-        n_estimators=50, random_state=0)
+    MumboClassifier(random_state=0)
     >>> print(clf.predict([[ 5.,  3.,  1.,  1.]]))
     [1]
     >>> views_ind = [[0, 2], [1, 3]]  # view 0: length data, view 1: width data
     >>> clf = MumboClassifier(random_state=0)
     >>> clf.fit(X, y, views_ind)  # doctest: +NORMALIZE_WHITESPACE
-    MumboClassifier(base_estimator=None, best_view_mode='edge',
-        n_estimators=50, random_state=0)
+    MumboClassifier(random_state=0)
     >>> print(clf.predict([[ 5.,  3.,  1.,  1.]]))
     [1]
 
@@ -143,13 +141,8 @@ class MumboClassifier(BaseEnsemble, ClassifierMixin, UBoosting):
     >>> base_estimator = DecisionTreeClassifier(max_depth=2)
     >>> clf = MumboClassifier(base_estimator=base_estimator, random_state=0)
     >>> clf.fit(X, y, views_ind)  # doctest: +NORMALIZE_WHITESPACE
-    MumboClassifier(base_estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,
-            criterion='gini', max_depth=2, max_features=None,
-            max_leaf_nodes=None, min_impurity_decrease=0.0,
-            min_impurity_split=None, min_samples_leaf=1, min_samples_split=2,
-            min_weight_fraction_leaf=0.0, presort='deprecated', random_state=None,
-            splitter='best'),
-        best_view_mode='edge', n_estimators=50, random_state=0)
+    MumboClassifier(base_estimator=DecisionTreeClassifier(max_depth=2),
+                    random_state=0)
     >>> print(clf.predict([[ 5.,  3.,  1.,  1.]]))
     [1]
 

multimodal/kernels/mvml.py

@@ -152,8 +152,7 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin, RegressorMixin):
     >>> clf.get_params()
     {'eta': 1, 'kernel': 'linear', 'kernel_params': None, 'learn_A': 1, 'learn_w': 0, 'lmbda': 0.1, 'n_loops': 6, 'nystrom_param': 1.0, 'precision': 0.0001}
     >>> clf.fit(X, y, views_ind)  # doctest: +NORMALIZE_WHITESPACE
-    MVML(eta=1, kernel='linear', kernel_params=None, learn_A=1, learn_w=0,
-       lmbda=0.1, n_loops=6, nystrom_param=1.0, precision=0.0001)
+    MVML()
     >>> print(clf.predict([[ 5.,  3.,  1.,  1.]]))
     0
 

setup.py

@@ -177,7 +177,7 @@ def setup_package():
                 'ensemble methods, boosting, kernel')
     packages = find_packages(exclude=['*.tests'])
     install_requires = ['scikit-learn>=0.24', 'numpy', 'scipy', 'cvxopt' ]
-    python_requires = '>=3.5'
+    python_requires = '>=3.6'
     extras_require = {
         'dev': ['pytest', 'pytest-cov'],
         'doc': ['sphinx', 'numpydoc', 'sphinx_gallery', 'matplotlib']}