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https://github.com/Doctorado-ML/Odte.git
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fix rc1
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9e5fe8c791
commit
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71
odte/Odte.py
71
odte/Odte.py
@ -26,35 +26,6 @@ from stree import Stree # type: ignore
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from ._version import __version__
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from ._version import __version__
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def _parallel_build_tree(
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base_estimator_: Stree,
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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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random_seed: int,
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boot_samples: int,
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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 = base_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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n_samples = X.shape[0]
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# bootstrap
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random_box = check_random_state(random_seed)
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indices = random_box.randint(0, n_samples, boot_samples)
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# update weights with the chosen samples
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weights_update = np.bincount(indices, minlength=n_samples)
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current_weights = weights * weights_update
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# random subspace
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features = Odte._get_random_subspace(X, y, max_features)
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# train the classifier
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bootstrap = X[indices, :]
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clf.fit(bootstrap[:, features], y[indices], current_weights[indices])
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return (clf, features)
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class Odte(BaseEnsemble, ClassifierMixin):
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class Odte(BaseEnsemble, ClassifierMixin):
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def __init__(
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def __init__(
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self,
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self,
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@ -135,15 +106,12 @@ class Odte(BaseEnsemble, ClassifierMixin):
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def _train(
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def _train(
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self, X: np.ndarray, y: np.ndarray, weights: np.ndarray
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self, X: np.ndarray, y: np.ndarray, weights: np.ndarray
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) -> Tuple[List[BaseEstimator], List[Tuple[int, ...]]]:
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) -> Tuple[List[BaseEstimator], List[Tuple[int, ...]]]:
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# np.random.RandomState(seed)
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n_samples = X.shape[0]
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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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boot_samples = self._get_bootstrap_n_samples(n_samples)
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estimator = []
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estimator = clone(self.base_estimator_)
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for i in range(self.n_estimators):
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estimator.append(clone(self.base_estimator_))
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return Parallel(n_jobs=self.n_jobs, prefer="threads")( # type: ignore
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return Parallel(n_jobs=self.n_jobs, prefer="threads")( # type: ignore
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delayed(_parallel_build_tree)(
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delayed(Odte._parallel_build_tree)(
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estimator[i],
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estimator,
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X,
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X,
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y,
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y,
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weights,
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weights,
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@ -152,11 +120,40 @@ class Odte(BaseEnsemble, ClassifierMixin):
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self.max_features_,
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self.max_features_,
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self.be_hyperparams,
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self.be_hyperparams,
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)
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)
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for i, random_seed in enumerate(
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for random_seed in range(
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range(self.random_state, self.random_state + self.n_estimators)
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self.random_state, self.random_state + self.n_estimators
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)
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)
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)
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)
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@staticmethod
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def _parallel_build_tree(
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base_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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random_seed: int,
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boot_samples: int,
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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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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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n_samples = X.shape[0]
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# bootstrap
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random_box = check_random_state(random_seed)
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indices = random_box.randint(0, n_samples, boot_samples)
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# update weights with the chosen samples
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weights_update = np.bincount(indices, minlength=n_samples)
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current_weights = weights * weights_update
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# random subspace
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features = Odte._get_random_subspace(X, y, max_features)
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# train the classifier
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bootstrap = X[indices, :]
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clf.fit(bootstrap[:, features], y[indices], current_weights[indices])
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return (clf, features)
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def _get_bootstrap_n_samples(self, n_samples: int) -> int:
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def _get_bootstrap_n_samples(self, n_samples: int) -> int:
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if self.max_samples is None:
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if self.max_samples is None:
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return n_samples
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return n_samples
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