From f35f09c9d81c6f17f9f05f2b0f2054b243a6f01c Mon Sep 17 00:00:00 2001 From: Provost Simon Date: Thu, 1 Aug 2024 01:23:11 +0100 Subject: [PATCH] refactor(estimators): improve lexico GB to be fully Scikit-learn processed instead of StarBoost [cd build] --- .../ensemble/lexico_gradient_boosting.md | 8 +- pdm.lock | 248 ++++++++---------- pyproject.toml | 5 +- .../lexico_gradient_boosting.py | 44 ++-- setup.py | 2 +- 5 files changed, 133 insertions(+), 174 deletions(-) diff --git a/docs/API/estimators/ensemble/lexico_gradient_boosting.md b/docs/API/estimators/ensemble/lexico_gradient_boosting.md index 2fb4a79..1ecdd43 100644 --- a/docs/API/estimators/ensemble/lexico_gradient_boosting.md +++ b/docs/API/estimators/ensemble/lexico_gradient_boosting.md @@ -10,7 +10,7 @@ LexicoGradientBoostingClassifier( max_depth: Optional[int] = 3, min_samples_split: int = 2, min_samples_leaf: int = 1, min_weight_fraction_leaf: float = 0.0, max_features: Optional[Union[int, str]] = None, random_state: Optional[int] = None, max_leaf_nodes: Optional[int] = None, - min_impurity_decrease: float = 0.0, ccp_alpha: float = 0.0, tree_flavor: bool = False, + min_impurity_decrease: float = 0.0, ccp_alpha: float = 0.0, n_estimators: int = 100, learning_rate: float = 0.1 ) ``` @@ -56,8 +56,7 @@ decision tree models capable of handling longitudinal data. - **max_leaf_nodes** (`Optional[int]`, default=None): The maximum number of leaf nodes in the tree. - **min_impurity_decrease** (`float`, optional, default=0.0): The minimum impurity decrease required for a node to be split. - **ccp_alpha** (`float`, optional, default=0.0): Complexity parameter used for Minimal Cost-Complexity Pruning. -- **tree_flavor** (`bool`, optional, default=False): Indicates whether to use a specific tree flavor. -- **n_estimators** (`int`, optional, default=100): The number of boosting stages to be run. +- **n_estimators** (`int`, optional, default=100): The number of DecisionTreeRegressor to have in the ensemble. - **learning_rate** (`float`, optional, default=0.1): Learning rate shrinks the contribution of each tree by `learning_rate`. ## Methods @@ -228,5 +227,4 @@ accuracy_score(y, y_pred) # (3) - **Ribeiro and Freitas (2020)**: - **Ribeiro, C. and Freitas, A., 2020, December.** A new random forest method for longitudinal data regression using a lexicographic bi-objective approach. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). -Here is the initial Python implementation of the Gradient Boosting algorithm: [Gradient Boosting Sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier) -As well as the implementation we are using: [Starboost Gradient Boosting](https://maxhalford.github.io/starboost/#classification) \ No newline at end of file +Here is the initial Python implementation of the Gradient Boosting algorithm: [Gradient Boosting Sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier) \ No newline at end of file diff --git a/pdm.lock b/pdm.lock index 61ba8c7..611d69e 100644 --- a/pdm.lock +++ b/pdm.lock @@ -5,7 +5,7 @@ groups = ["default", "doc", "lint", "test"] strategy = ["cross_platform", "inherit_metadata"] lock_version = "4.4.2" -content_hash = 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It offers specialised tools to tackle challenges of repeated measures data, ideal for (med.) researchers, data scientists, & analysts." authors = [ {name = "Provost Simon", email = "simon.gilbert.provost@gmail.com"}, @@ -23,8 +23,7 @@ dependencies = [ "rich>=13.6.0", "joblib>=0.11", "deep-forest>=0.1.7", - "starboost==0.0.2", - "scikit-lexicographical-trees==0.0.2", + "scikit-lexicographical-trees==0.0.4", ] requires-python = ">=3.9,<3.10" readme = "README.md" diff --git a/scikit_longitudinal/estimators/ensemble/lexicographical/lexico_gradient_boosting.py b/scikit_longitudinal/estimators/ensemble/lexicographical/lexico_gradient_boosting.py index 69b3117..d680ba4 100644 --- a/scikit_longitudinal/estimators/ensemble/lexicographical/lexico_gradient_boosting.py +++ b/scikit_longitudinal/estimators/ensemble/lexicographical/lexico_gradient_boosting.py @@ -5,11 +5,8 @@ import numpy as np from overrides import override from sklearn.utils.multiclass import unique_labels -from starboost import BoostingClassifier -from scikit_longitudinal.estimators.trees.lexicographical.lexico_decision_tree_regressor import ( - LexicoDecisionTreeRegressor, -) +from sklearn.ensemble import GradientBoostingClassifier from scikit_longitudinal.templates import CustomClassifierMixinEstimator @@ -137,7 +134,6 @@ def __init__( max_leaf_nodes: Optional[int] = None, min_impurity_decrease: float = 0.0, ccp_alpha: float = 0.0, - tree_flavor: bool = False, n_estimators: int = 100, learning_rate: float = 0.1, ): @@ -154,12 +150,10 @@ def __init__( self.min_impurity_decrease = min_impurity_decrease self.ccp_alpha = ccp_alpha self.random_state = random_state - self.tree_flavor = tree_flavor self.n_estimators = n_estimators self.learning_rate = learning_rate self._lexico_gradient_boosting = None - self._base_estimator = None self.classes_ = None @ensure_valid_state @@ -181,29 +175,10 @@ def _fit(self, X: np.ndarray, y: np.ndarray) -> "LexicoGradientBoostingClassifie If there are less than or equal to 1 feature group. """ - _base_estimator = LexicoDecisionTreeRegressor( - features_group=self.features_group, - threshold_gain=self.threshold_gain, - criterion=self.criterion, + self._lexico_gradient_boosting = GradientBoostingClassifier( splitter=self.splitter, - max_depth=self.max_depth, - min_samples_split=self.min_samples_split, - min_samples_leaf=self.min_samples_leaf, - min_weight_fraction_leaf=self.min_weight_fraction_leaf, - max_features=self.max_features, - random_state=self.random_state, - max_leaf_nodes=self.max_leaf_nodes, - min_impurity_decrease=self.min_impurity_decrease, - ccp_alpha=self.ccp_alpha, - ) - - self._base_estimator = _base_estimator - self._lexico_gradient_boosting = BoostingClassifier( - base_estimator=_base_estimator, - n_estimators=self.n_estimators, - learning_rate=self.learning_rate, - tree_flavor=self.tree_flavor, - random_state=self.random_state, + threshold_gain=self.threshold_gain, + features_group=self.features_group, ) if self.classes_ is None: @@ -242,3 +217,14 @@ def _predict_proba(self, X: np.ndarray) -> np.ndarray: """ return self._lexico_gradient_boosting.predict_proba(X) + + @property + def feature_importances_(self) -> np.ndarray: + """Return the feature importances. + + Returns: + np.ndarray: + The feature importances. + + """ + return self._lexico_gradient_boosting.feature_importances_ diff --git a/setup.py b/setup.py index 4fcc4fa..bd35b0f 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( # pragma: no cover name="Scikit-longitudinal", - version="0.0.5", + version="0.0.6", long_description=open('README.md').read(), long_description_content_type='text/markdown', url="https://github.com/simonprovost/scikit-longitudinal",