"""Submodule wrapping Extra Trees for edge prediction."""
from typing import Dict, Any
from sklearn.ensemble import ExtraTreesClassifier
from embiggen.edge_prediction.edge_prediction_sklearn.decision_tree_edge_prediction import DecisionTreeEdgePrediction
from embiggen.edge_prediction.edge_prediction_sklearn.sklearn_edge_prediction_adapter import SklearnEdgePredictionAdapter
from embiggen.utils.normalize_kwargs import normalize_kwargs

class ExtraTreesEdgePrediction(SklearnEdgePredictionAdapter):
    """Create wrapper over Sklearn Extra Trees classifier for edge 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="auto",
        max_leaf_nodes=None,
        min_impurity_decrease=0.,
        bootstrap=True,
        oob_score=False,
        n_jobs=-1,
        verbose=0,
        warm_start=False,
        ccp_alpha=0.0,
        max_samples=None,
        edge_embedding_method: str = "Concatenate",
        training_unbalance_rate: float = 1.0,
        training_sample_only_edges_with_heterogeneous_node_types: bool = False,
        use_edge_metrics: bool = False,
        use_scale_free_distribution: bool = True,
        prediction_batch_size: int = 2**12,
        random_state: int = 42
    ):
        """Create the Extra Trees for Edge  Prediction."""
        self._tree_kwargs = normalize_kwargs(dict(
            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,
            ccp_alpha = ccp_alpha,
            max_samples = max_samples,
        ))

        super().__init__(
            ExtraTreesClassifier(
                **self._tree_kwargs
            ),
            edge_embedding_method=edge_embedding_method,
            training_unbalance_rate=training_unbalance_rate,
            use_edge_metrics=use_edge_metrics,
            use_scale_free_distribution=use_scale_free_distribution,
            training_sample_only_edges_with_heterogeneous_node_types=training_sample_only_edges_with_heterogeneous_node_types,
            prediction_batch_size=prediction_batch_size,
            random_state=random_state
        )

    @classmethod
    def smoke_test_parameters(cls) -> Dict[str, Any]:
        """Returns parameters for smoke test."""
        return dict(
            **DecisionTreeEdgePrediction.smoke_test_parameters(),
            n_estimators=1
        )

    def parameters(self) -> Dict[str, Any]:
        """Returns parameters used for this model."""
        return {
            **super().parameters(),
            **self._tree_kwargs
        }
    
    @classmethod
    def model_name(cls) -> str:
        return "Extra Trees Classifier"