First try to fix initialization issue

This commit is contained in:
Ricardo Montañana Gómez 2022-02-22 20:40:35 +01:00
parent aff96bb97d
commit cd7c7f3938
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
3 changed files with 52 additions and 35 deletions

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@ -26,6 +26,35 @@ from stree import Stree # type: ignore
from ._version import __version__
def _parallel_build_tree(
base_estimator_: Stree,
X: np.ndarray,
y: np.ndarray,
weights: np.ndarray,
random_box: np.random.mtrand.RandomState,
random_seed: int,
boot_samples: int,
max_features: int,
hyperparams: str,
) -> Tuple[BaseEstimator, Tuple[int, ...]]:
clf = base_estimator_
hyperparams_ = json.loads(hyperparams)
hyperparams_.update(dict(random_state=random_seed))
clf.set_params(**hyperparams_)
n_samples = X.shape[0]
# bootstrap
indices = random_box.randint(0, n_samples, boot_samples)
# update weights with the chosen samples
weights_update = np.bincount(indices, minlength=n_samples)
current_weights = weights * weights_update
# random subspace
features = Odte._get_random_subspace(X, y, max_features)
# train the classifier
bootstrap = X[indices, :]
clf.fit(bootstrap[:, features], y[indices], current_weights[indices])
return (clf, features)
class Odte(BaseEnsemble, ClassifierMixin):
def __init__(
self,
@ -109,45 +138,18 @@ class Odte(BaseEnsemble, ClassifierMixin):
self.leaves_ = tleaves / self.n_estimators
self.nodes_ = tnodes / self.n_estimators
@staticmethod
def _parallel_build_tree(
base_estimator_: Stree,
X: np.ndarray,
y: np.ndarray,
weights: np.ndarray,
random_box: np.random.mtrand.RandomState,
random_seed: int,
boot_samples: int,
max_features: int,
hyperparams: str,
) -> Tuple[BaseEstimator, Tuple[int, ...]]:
clf = clone(base_estimator_)
hyperparams_ = json.loads(hyperparams)
hyperparams_.update(dict(random_state=random_seed))
clf.set_params(**hyperparams_)
n_samples = X.shape[0]
# bootstrap
indices = random_box.randint(0, n_samples, boot_samples)
# update weights with the chosen samples
weights_update = np.bincount(indices, minlength=n_samples)
current_weights = weights * weights_update
# random subspace
features = Odte._get_random_subspace(X, y, max_features)
# train the classifier
bootstrap = X[indices, :]
clf.fit(bootstrap[:, features], y[indices], current_weights[indices])
return (clf, features)
def _train(
self, X: np.ndarray, y: np.ndarray, weights: np.ndarray
) -> Tuple[List[BaseEstimator], List[Tuple[int, ...]]]:
random_box = self._initialize_random()
n_samples = X.shape[0]
boot_samples = self._get_bootstrap_n_samples(n_samples)
clf = clone(self.base_estimator_)
estimator = []
for i in range(self.n_estimators):
estimator.append(clone(self.base_estimator_))
return Parallel(n_jobs=self.n_jobs, prefer="threads")( # type: ignore
delayed(Odte._parallel_build_tree)(
clf,
delayed(_parallel_build_tree)(
estimator[i],
X,
y,
weights,
@ -157,8 +159,11 @@ class Odte(BaseEnsemble, ClassifierMixin):
self.max_features_,
self.be_hyperparams,
)
for random_seed in range(
self.random_state, self.random_state + self.n_estimators
for random_seed, i in zip(
range(
self.random_state, self.random_state + self.n_estimators
),
range(self.n_estimators),
)
)

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@ -1 +1 @@
__version__ = "0.3.1"
__version__ = "0.3.2"

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@ -257,3 +257,15 @@ class Odte_test(unittest.TestCase):
def test_version(self):
tclf = Odte()
self.assertEqual(__version__, tclf.version())
def test_parallel_score(self):
tclf_p = Odte(
n_jobs=-1, random_state=self._random_state, n_estimators=30
)
tclf_s = Odte(
n_jobs=1, random_state=self._random_state, n_estimators=30
)
X, y = load_dataset(self._random_state, n_features=56, n_samples=1500)
tclf_p.fit(X, y)
tclf_s.fit(X, y)
self.assertAlmostEqual(tclf_p.score(X, y), tclf_s.score(X, y))