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https://github.com/Doctorado-ML/Odte.git
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Merge pull request #6 from Doctorado-ML/parallel_init
Parallel init error
This commit is contained in:
Ricardo Montañana Gómez
GitHub
commit
98cadc7eeb
58
odte/Odte.py
58
odte/Odte.py
@ -7,7 +7,6 @@ as well
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"""
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from __future__ import annotations
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import random
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import sys
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import json
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from math import factorial
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from typing import Union, Optional, Tuple, List, Set
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@ -16,6 +15,7 @@ from sklearn.utils.multiclass import ( # type: ignore
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check_classification_targets,
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)
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from sklearn.base import clone, BaseEstimator, ClassifierMixin # type: ignore
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from sklearn.utils import check_random_state
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from sklearn.ensemble import BaseEnsemble # type: ignore
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from sklearn.utils.validation import ( # type: ignore
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check_is_fitted,
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@ -54,12 +54,6 @@ class Odte(BaseEnsemble, ClassifierMixin):
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def version() -> str:
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return __version__
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def _initialize_random(self) -> np.random.mtrand.RandomState:
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if self.random_state is None:
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self.random_state = random.randint(0, sys.maxsize)
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return np.random.mtrand._rand
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return np.random.RandomState(self.random_state)
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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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super()._validate_estimator(
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@ -109,13 +103,34 @@ class Odte(BaseEnsemble, ClassifierMixin):
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self.leaves_ = tleaves / self.n_estimators
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self.nodes_ = tnodes / self.n_estimators
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def _train(
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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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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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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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X,
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y,
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weights,
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random_seed,
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boot_samples,
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self.max_features_,
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self.be_hyperparams,
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)
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for random_seed in range(
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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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@staticmethod
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def _parallel_build_tree(
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base_estimator_: Stree,
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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_box: np.random.mtrand.RandomState,
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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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@ -127,6 +142,7 @@ class Odte(BaseEnsemble, ClassifierMixin):
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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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@ -138,30 +154,6 @@ class Odte(BaseEnsemble, ClassifierMixin):
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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 _train(
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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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random_box = self._initialize_random()
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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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clf = clone(self.base_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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clf,
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X,
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y,
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weights,
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random_box,
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random_seed,
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boot_samples,
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self.max_features_,
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self.be_hyperparams,
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)
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for random_seed in range(
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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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def _get_bootstrap_n_samples(self, n_samples: int) -> int:
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if self.max_samples is None:
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return n_samples
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@ -1 +1 @@
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__version__ = "0.3.1"
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__version__ = "0.3.2"
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@ -54,20 +54,6 @@ class Odte_test(unittest.TestCase):
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self.assertListEqual(expected, list(computed))
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# print(f"{list(computed)},")
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def test_initialize_random(self):
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expected = [37, 235, 908]
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tclf = Odte(random_state=self._random_state)
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box = tclf._initialize_random()
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computed = box.randint(0, 1000, 3)
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self.assertListEqual(expected, computed.tolist())
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# test None
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tclf = Odte(random_state=None)
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box = tclf._initialize_random()
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computed = box.randint(101, 1000, 3)
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for value in computed.tolist():
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self.assertGreaterEqual(value, 101)
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self.assertLessEqual(value, 1000)
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def test_bogus_max_features(self):
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values = ["duck", -0.1, 0.0]
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for max_features in values:
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@ -124,7 +110,7 @@ class Odte_test(unittest.TestCase):
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def test_score(self):
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X, y = load_dataset(self._random_state)
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expected = 0.9513333333333334
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expected = 0.9533333333333334
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tclf = Odte(
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random_state=self._random_state,
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max_features=None,
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@ -136,19 +122,18 @@ class Odte_test(unittest.TestCase):
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def test_score_splitter_max_features(self):
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X, y = load_dataset(self._random_state, n_features=16, n_samples=500)
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results = [
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0.948,
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0.924,
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0.926,
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0.94,
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0.932,
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0.936,
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0.962,
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0.962,
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0.962,
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0.962,
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0.962,
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0.962,
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0.962,
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0.958, # best auto
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0.942, # random auto
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0.932, # trandom auto
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0.95, # mutual auto
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0.944, # iwss auto
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0.946, # cfs auto
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0.97, # best None
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0.97, # random None
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0.97, # trandom None
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0.97, # mutual None
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0.97, # iwss None
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0.97, # cfs None
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]
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random.seed(self._random_state)
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for max_features in ["auto", None]:
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@ -207,16 +192,25 @@ class Odte_test(unittest.TestCase):
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tclf = Odte(
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base_estimator=Stree(),
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random_state=self._random_state,
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n_estimators=3,
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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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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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X, y = load_dataset(self._random_state, n_features=16, n_samples=500)
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tclf.fit(X, y)
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self.assertAlmostEqual(6.0, tclf.depth_)
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self.assertAlmostEqual(9.333333333333334, tclf.leaves_)
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self.assertAlmostEqual(17.666666666666668, tclf.nodes_)
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nodes, leaves = tclf.nodes_leaves()
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self.assertAlmostEqual(9.333333333333334, leaves)
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self.assertAlmostEqual(17.666666666666668, nodes)
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tclf_p.fit(X, y)
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for clf in [tclf, tclf_p]:
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self.assertAlmostEqual(5.8, clf.depth_)
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self.assertAlmostEqual(9.4, clf.leaves_)
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self.assertAlmostEqual(17.8, clf.nodes_)
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nodes, leaves = clf.nodes_leaves()
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self.assertAlmostEqual(9.4, leaves)
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self.assertAlmostEqual(17.8, nodes)
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def test_nodes_leaves_SVC(self):
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tclf = Odte(
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@ -257,3 +251,15 @@ class Odte_test(unittest.TestCase):
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def test_version(self):
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tclf = Odte()
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self.assertEqual(__version__, tclf.version())
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def test_parallel_score(self):
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tclf_p = Odte(
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n_jobs=-1, random_state=self._random_state, n_estimators=30
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)
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tclf_s = Odte(
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n_jobs=1, random_state=self._random_state, n_estimators=30
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)
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X, y = load_dataset(self._random_state, n_features=56, n_samples=1500)
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tclf_p.fit(X, y)
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tclf_s.fit(X, y)
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self.assertAlmostEqual(tclf_p.score(X, y), tclf_s.score(X, y))
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