From 32ab7a9e3b15b0328755e104d77a84ea3c262801 Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Thu, 25 Feb 2021 08:15:49 -0500
Subject: [PATCH] compatible with sklearn 00.24 & pytest
---
multimodal/boosting/combo.py | 13 ++++---------
multimodal/boosting/mumbo.py | 15 ++++-----------
multimodal/kernels/mvml.py | 3 +--
setup.py | 2 +-
4 files changed, 10 insertions(+), 23 deletions(-)
diff --git a/multimodal/boosting/combo.py b/multimodal/boosting/combo.py
index c56ebf2..47c7c59 100644
--- a/multimodal/boosting/combo.py
+++ b/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]
diff --git a/multimodal/boosting/mumbo.py b/multimodal/boosting/mumbo.py
index 255c4f7..62f0c5a 100644
--- a/multimodal/boosting/mumbo.py
+++ b/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]
diff --git a/multimodal/kernels/mvml.py b/multimodal/kernels/mvml.py
index 0636077..8444ff3 100644
--- a/multimodal/kernels/mvml.py
+++ b/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
diff --git a/setup.py b/setup.py
index a4e76c4..0bb17e2 100644
--- a/setup.py
+++ b/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']}
--
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