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https://github.com/Doctorado-ML/Odte.git
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Fix np.random initialization
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parent
cd7c7f3938
commit
3766886190
19
odte/Odte.py
19
odte/Odte.py
@ -16,6 +16,7 @@ from sklearn.utils.multiclass import ( # type: ignore
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check_classification_targets,
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check_classification_targets,
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)
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)
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from sklearn.base import clone, BaseEstimator, ClassifierMixin # type: ignore
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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.ensemble import BaseEnsemble # type: ignore
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from sklearn.utils.validation import ( # type: ignore
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from sklearn.utils.validation import ( # type: ignore
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check_is_fitted,
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check_is_fitted,
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@ -31,7 +32,6 @@ def _parallel_build_tree(
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X: np.ndarray,
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X: np.ndarray,
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y: np.ndarray,
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y: np.ndarray,
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weights: 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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random_seed: int,
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boot_samples: int,
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boot_samples: int,
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max_features: int,
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max_features: int,
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@ -43,6 +43,7 @@ def _parallel_build_tree(
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clf.set_params(**hyperparams_)
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clf.set_params(**hyperparams_)
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n_samples = X.shape[0]
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n_samples = X.shape[0]
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# bootstrap
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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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indices = random_box.randint(0, n_samples, boot_samples)
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# update weights with the chosen 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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weights_update = np.bincount(indices, minlength=n_samples)
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@ -83,12 +84,6 @@ class Odte(BaseEnsemble, ClassifierMixin):
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def version() -> str:
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def version() -> str:
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return __version__
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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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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 base_estimator_ attribute."""
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super()._validate_estimator(
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super()._validate_estimator(
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@ -141,7 +136,7 @@ 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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random_box = self._initialize_random()
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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 = []
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@ -153,17 +148,13 @@ class Odte(BaseEnsemble, ClassifierMixin):
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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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random_box,
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random_seed,
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random_seed,
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boot_samples,
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boot_samples,
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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 random_seed, i in zip(
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for i, random_seed in enumerate(
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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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range(self.n_estimators),
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)
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)
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)
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)
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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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self.assertListEqual(expected, list(computed))
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# print(f"{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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def test_bogus_max_features(self):
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values = ["duck", -0.1, 0.0]
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values = ["duck", -0.1, 0.0]
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for max_features in values:
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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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def test_score(self):
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X, y = load_dataset(self._random_state)
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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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tclf = Odte(
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random_state=self._random_state,
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random_state=self._random_state,
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max_features=None,
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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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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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X, y = load_dataset(self._random_state, n_features=16, n_samples=500)
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results = [
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results = [
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0.948,
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0.958, # best auto
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0.924,
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0.942, # random auto
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0.926,
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0.932, # trandom auto
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0.94,
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0.95, # mutual auto
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0.932,
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0.944, # iwss auto
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0.936,
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0.946, # cfs auto
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0.962,
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0.97, # best None
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0.962,
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0.97, # random None
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0.962,
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0.97, # trandom None
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0.962,
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0.97, # mutual None
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0.962,
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0.97, # iwss None
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0.962,
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0.97, # cfs None
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0.962,
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]
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]
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random.seed(self._random_state)
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random.seed(self._random_state)
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for max_features in ["auto", None]:
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for max_features in ["auto", None]:
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@ -208,15 +193,32 @@ class Odte_test(unittest.TestCase):
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base_estimator=Stree(),
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base_estimator=Stree(),
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random_state=self._random_state,
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random_state=self._random_state,
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n_estimators=3,
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n_estimators=3,
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n_jobs=1,
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)
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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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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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tclf.fit(X, y)
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self.assertAlmostEqual(6.0, tclf.depth_)
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self.assertAlmostEqual(6.333333333333333, tclf.depth_)
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self.assertAlmostEqual(9.333333333333334, tclf.leaves_)
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self.assertAlmostEqual(10.0, tclf.leaves_)
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self.assertAlmostEqual(17.666666666666668, tclf.nodes_)
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self.assertAlmostEqual(19.0, tclf.nodes_)
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nodes, leaves = tclf.nodes_leaves()
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nodes, leaves = tclf.nodes_leaves()
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self.assertAlmostEqual(9.333333333333334, leaves)
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self.assertAlmostEqual(10.0, leaves)
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self.assertAlmostEqual(17.666666666666668, nodes)
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self.assertAlmostEqual(19, nodes)
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def test_nodes_leaves_depth_parallel(self):
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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_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(6.333333333333333, tclf.depth_)
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self.assertAlmostEqual(10.0, tclf.leaves_)
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self.assertAlmostEqual(19.0, tclf.nodes_)
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nodes, leaves = tclf.nodes_leaves()
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self.assertAlmostEqual(10.0, leaves)
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self.assertAlmostEqual(19, nodes)
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def test_nodes_leaves_SVC(self):
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def test_nodes_leaves_SVC(self):
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tclf = Odte(
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tclf = Odte(
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