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https://github.com/Doctorado-ML/Odte.git
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Update to scikit-learn 1.2
This commit is contained in:
Ricardo Montañana Gómez
parent
7300bd66db
commit
cabf926eb1
GPG Key ID:
46064262FD9A7ADE
@ -40,9 +40,9 @@
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"clf = {}\n",
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"clf[\"stree\"] = Stree(random_state=random_state, max_depth=5)\n",
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"clf[\"stree\"].set_params(**dict(splitter=\"best\", kernel=\"linear\", max_features=\"auto\"))\n",
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"clf[\"odte\"] = Odte(n_jobs=-1, base_estimator=clf[\"stree\"], random_state=random_state, n_estimators=n_estimators, max_features=.8)\n",
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"clf[\"adaboost\"] = AdaBoostClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators, random_state=random_state, algorithm=\"SAMME\")\n",
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"clf[\"bagging\"] = BaggingClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators)"
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"clf[\"odte\"] = Odte(n_jobs=-1, estimator=clf[\"stree\"], random_state=random_state, n_estimators=n_estimators, max_features=.8)\n",
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"clf[\"adaboost\"] = AdaBoostClassifier(estimator=clf[\"stree\"], n_estimators=n_estimators, random_state=random_state, algorithm=\"SAMME\")\n",
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"clf[\"bagging\"] = BaggingClassifier(estimator=clf[\"stree\"], n_estimators=n_estimators)"
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]
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},
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{
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@ -82,8 +82,8 @@
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"clf = {}\n",
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"clf[\"stree\"] = Stree(random_state=random_state, max_depth=3)\n",
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"clf[\"odte\"] = Odte(n_jobs=-1, random_state=random_state, n_estimators=n_estimators, max_features=1.0)\n",
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"clf[\"adaboost\"] = AdaBoostClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators, random_state=random_state, algorithm=\"SAMME\")\n",
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"clf[\"bagging\"] = BaggingClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators)"
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"clf[\"adaboost\"] = AdaBoostClassifier(estimator=clf[\"stree\"], n_estimators=n_estimators, random_state=random_state, algorithm=\"SAMME\")\n",
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"clf[\"bagging\"] = BaggingClassifier(estimator=clf[\"stree\"], n_estimators=n_estimators)"
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]
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},
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{
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16
odte/Odte.py
16
odte/Odte.py
@ -31,7 +31,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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self,
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# n_jobs = -1 to use all available cores
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n_jobs: int = -1,
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base_estimator: BaseEstimator = None,
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estimator: BaseEstimator = None,
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random_state: int = 0,
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max_features: Optional[Union[str, int, float]] = None,
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max_samples: Optional[Union[int, float]] = None,
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@ -39,10 +39,10 @@ class Odte(BaseEnsemble, ClassifierMixin):
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be_hyperparams: str = "{}",
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):
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super().__init__(
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base_estimator=base_estimator,
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estimator=estimator,
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n_estimators=n_estimators,
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)
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self.base_estimator = base_estimator
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self.estimator = estimator
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self.n_jobs = n_jobs
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self.n_estimators = n_estimators
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self.random_state = random_state
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@ -55,7 +55,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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return __version__
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def _validate_estimator(self) -> None:
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"""Check the estimator and set the base_estimator_ attribute."""
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"""Check the estimator and set the estimator_ attribute."""
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super()._validate_estimator(
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default=Stree(random_state=self.random_state)
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)
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@ -79,7 +79,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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# Initialize computed parameters
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# Build the estimator
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self.max_features_ = self._initialize_max_features()
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# build base_estimator_
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# build estimator_
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self._validate_estimator()
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self.classes_, y = np.unique(y, return_inverse=True)
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self.n_classes_: int = self.classes_.shape[0]
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@ -108,7 +108,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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) -> Tuple[List[BaseEstimator], List[Tuple[int, ...]]]:
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n_samples = X.shape[0]
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boot_samples = self._get_bootstrap_n_samples(n_samples)
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estimator = clone(self.base_estimator_)
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estimator = clone(self.estimator_)
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return Parallel(n_jobs=self.n_jobs, prefer="threads")( # type: ignore
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delayed(Odte._parallel_build_tree)(
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estimator,
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@ -127,7 +127,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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@staticmethod
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def _parallel_build_tree(
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base_estimator_: BaseEstimator,
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estimator_: BaseEstimator,
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X: np.ndarray,
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y: np.ndarray,
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weights: np.ndarray,
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@ -136,7 +136,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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max_features: int,
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hyperparams: str,
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) -> Tuple[BaseEstimator, Tuple[int, ...]]:
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clf = clone(base_estimator_)
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clf = clone(estimator_)
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hyperparams_ = json.loads(hyperparams)
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hyperparams_.update(dict(random_state=random_seed))
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clf.set_params(**hyperparams_)
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@ -76,15 +76,15 @@ class Odte_test(unittest.TestCase):
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X, y = [[1, 2], [5, 6], [9, 10], [16, 17]], [0, 1, 1, 2]
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expected = [0, 1, 1, 2]
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tclf = Odte(
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base_estimator=Stree(),
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estimator=Stree(),
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random_state=self._random_state,
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n_estimators=10,
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n_jobs=-1,
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)
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tclf.set_params(
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**dict(
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base_estimator__kernel="rbf",
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base_estimator__random_state=self._random_state,
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estimator__kernel="rbf",
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estimator__random_state=self._random_state,
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)
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)
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computed = tclf.fit(X, y).predict(X)
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@ -96,14 +96,14 @@ class Odte_test(unittest.TestCase):
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X, y = load_dataset(self._random_state)
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expected = y
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tclf = Odte(
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base_estimator=Stree(),
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estimator=Stree(),
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random_state=self._random_state,
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max_features=1.0,
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max_samples=0.1,
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)
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tclf.set_params(
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**dict(
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base_estimator__kernel="linear",
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estimator__kernel="linear",
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)
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)
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computed = tclf.fit(X, y).predict(X)
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@ -146,16 +146,16 @@ class Odte_test(unittest.TestCase):
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"cfs",
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]:
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tclf = Odte(
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base_estimator=Stree(),
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estimator=Stree(),
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random_state=self._random_state,
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n_estimators=3,
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n_jobs=1,
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)
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tclf.set_params(
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**dict(
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base_estimator__max_features=max_features,
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base_estimator__splitter=splitter,
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base_estimator__random_state=self._random_state,
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estimator__max_features=max_features,
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estimator__splitter=splitter,
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estimator__random_state=self._random_state,
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)
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)
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expected = results.pop(0)
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@ -182,7 +182,7 @@ class Odte_test(unittest.TestCase):
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def test_nodes_leaves_not_fitted(self):
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tclf = Odte(
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base_estimator=Stree(),
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estimator=Stree(),
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random_state=self._random_state,
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n_estimators=3,
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)
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@ -191,13 +191,13 @@ class Odte_test(unittest.TestCase):
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def test_nodes_leaves_depth(self):
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tclf = Odte(
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base_estimator=Stree(),
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estimator=Stree(),
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random_state=self._random_state,
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n_estimators=5,
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n_jobs=1,
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)
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tclf_p = Odte(
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base_estimator=Stree(),
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estimator=Stree(),
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random_state=self._random_state,
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n_estimators=5,
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n_jobs=-1,
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@ -215,7 +215,7 @@ class Odte_test(unittest.TestCase):
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def test_nodes_leaves_SVC(self):
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tclf = Odte(
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base_estimator=SVC(),
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estimator=SVC(),
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random_state=self._random_state,
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n_estimators=3,
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)
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@ -227,7 +227,7 @@ class Odte_test(unittest.TestCase):
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self.assertAlmostEqual(0.0, leaves)
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self.assertAlmostEqual(0.0, nodes)
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def test_base_estimator_hyperparams(self):
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def test_estimator_hyperparams(self):
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data = [
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(Stree(), {"max_features": 7, "max_depth": 2}),
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(SVC(), {"kernel": "linear", "cache_size": 100}),
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@ -235,7 +235,7 @@ class Odte_test(unittest.TestCase):
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for clf, hyperparams in data:
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hyperparams_ = json.dumps(hyperparams)
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tclf = Odte(
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base_estimator=clf,
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estimator=clf,
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random_state=self._random_state,
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n_estimators=3,
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be_hyperparams=hyperparams_,
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