embiggen/node_label_prediction/node_label_prediction_sklearn/extra_trees_node_label_prediction.py
"""Submodule wrapping Extra Trees for node label prediction."""
from typing import Dict, Any
from sklearn.ensemble import ExtraTreesClassifier
from embiggen.node_label_prediction.node_label_prediction_sklearn.decision_tree_node_label_prediction import DecisionTreeNodeLabelPrediction
from embiggen.node_label_prediction.node_label_prediction_sklearn.sklearn_node_label_prediction_adapter import SklearnNodeLabelPredictionAdapter
class ExtraTreesNodeLabelPrediction(SklearnNodeLabelPredictionAdapter):
"""Create wrapper over Sklearn Extra Trees classifier for node label prediction."""
def __init__(
self,
n_estimators: int = 1000,
criterion: str = "gini",
max_depth: int = 10,
min_samples_split: int = 2,
min_samples_leaf: int = 1,
min_weight_fraction_leaf: float = 0.,
max_features="sqrt",
max_leaf_nodes=None,
min_impurity_decrease=0.,
bootstrap=True,
oob_score=False,
n_jobs=-1,
verbose=0,
warm_start=False,
class_weight="balanced",
ccp_alpha=0.0,
max_samples=None,
random_state: int = 42
):
"""Create the Extra Trees for Edge Prediction."""
self._n_estimators = n_estimators
self._criterion = criterion
self._max_depth = max_depth
self._min_samples_split = min_samples_split
self._min_samples_leaf = min_samples_leaf
self._min_weight_fraction_leaf = min_weight_fraction_leaf
self._max_features = max_features
self._max_leaf_nodes = max_leaf_nodes
self._min_impurity_decrease = min_impurity_decrease
self._bootstrap = bootstrap
self._oob_score = oob_score
self._n_jobs = n_jobs
self._verbose = verbose
self._warm_start = warm_start
self._class_weight = class_weight
self._ccp_alpha = ccp_alpha
self._max_samples = max_samples
super().__init__(
ExtraTreesClassifier(
n_estimators=n_estimators,
criterion=criterion,
max_depth=max_depth,
min_samples_split=min_samples_split,
min_samples_leaf=min_samples_leaf,
min_weight_fraction_leaf=min_weight_fraction_leaf,
max_features=max_features,
max_leaf_nodes=max_leaf_nodes,
min_impurity_decrease=min_impurity_decrease,
bootstrap=bootstrap,
oob_score=oob_score,
n_jobs=n_jobs,
random_state=random_state,
verbose=verbose,
warm_start=warm_start,
class_weight=class_weight,
ccp_alpha=ccp_alpha,
max_samples=max_samples
),
random_state
)
@classmethod
def smoke_test_parameters(cls) -> Dict[str, Any]:
"""Returns parameters for smoke test."""
return dict(
**DecisionTreeNodeLabelPrediction.smoke_test_parameters(),
n_estimators=1
)
def parameters(self) -> Dict[str, Any]:
"""Returns parameters used for this model."""
return {
**super().parameters(),
**dict(
n_estimators = self._n_estimators,
criterion = self._criterion,
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,
max_leaf_nodes = self._max_leaf_nodes,
min_impurity_decrease = self._min_impurity_decrease,
bootstrap = self._bootstrap,
oob_score = self._oob_score,
n_jobs = self._n_jobs,
verbose = self._verbose,
warm_start = self._warm_start,
class_weight = self._class_weight,
ccp_alpha = self._ccp_alpha,
max_samples = self._max_samples,
)
}
@classmethod
def model_name(cls) -> str:
return "Extra Trees Classifier"
@classmethod
def supports_multilabel_prediction(cls) -> bool:
"""Returns whether the model supports multilabel prediction."""
return True